Nearby Samples Research Articles

Exposure to sulfur in soil explains pigmentation by pheomelanin in birds inhabiting Iceland

Melanins are the most common pigments in birds and mammals. The synthesis of the sulfurated form of melanin, termed pheomelanin, is promoted by high levels of thiols in melanocytes. Exposure to elemental sulfur in the environment can potentially influence intracellular thiol levels in terrestrial animals, but this has never been investigated in the wild. This is particularly relevant in areas with a high volcanic activity that facilitate sulfur exposure. We explored for an association between the pheomelanin content of feathers in 35 breeding birds from 13 phylogenetically diverse species and 29 locations in Iceland, and the sulfur content of nearby soil samples. After controlling for the effect of phylogenetic relatedness between species, we found that the pheomelanin content of feathers was significantly correlated with the sulfur content of soil, indicating that pheomelanin production increases with sulfur exposure. Our results suggest that environmental exposure to relatively high levels of elemental sulfur promotes the expression of pheomelanin-based pigmentation phenotypes in birds, likely by an influence on intracellular thiols during development. This is the first evidence that soil chemistry can affect the phenotype of animals. Highly volcanic areas such as Iceland are thus potential sources of global phenotypic diversity by promoting sulfur exposure in animals.

Forecast of cosmological constraints with type Ia supernovae from the Chinese Space Station Telescope

The 2-m aperture Chinese Space Station Telescope (CSST), which observes at wavelengths ranging from 255 to 1000 nm, is expected to start science operations in 2024. An ultra-deep field observation program covering approximately 10 deg2 is proposed with supernovae (SNe) and other transients as one of its primary science drivers. This paper presents the simulated detection results of type Ia supernovae (SNe Ia) and explores the impact of new datasets on the determinations of cosmological parameters. The simulated observations are conducted with an exposure time of 150 s and cadences of 10, 20, and 30 d. The survey mode covering a total of 80 observations but with a random cadence in the range of 4 to 14 d is also explored. Our simulation results indicate that the CSST can detect up to ∼ 1800 SNe Ia at z < 1.3. The simulated SNe Ia are then used to constrain the cosmological parameters. The constraint on Ωm can be improved by 37.5% using the 10-d cadence sample in comparison with the Pantheon sample. A deeper measurement simulation with a 300 s exposure time together with the Pantheon sample improves the current constraints on Ωm by 58.3% and ω by 47.7%. Taking future lager sets of nearby SN Ia sample form ground-based surveys (i.e., N ∼ 3400) into consideration, the constraints on ω can be improved by 59.1%. The CSST ultra-deep field observation program is expected to discover large amounts of SNe Ia over a broad redshift span and enhance our understanding of the nature of dark energy.

A Lightweight Multitask Learning Model With Adaptive Loss Balance for Tropical Cyclone Intensity and Size Estimation

Accurate tropical cyclone (TC) intensity and size estimation are key in disaster management and prevention. While great breakthroughs have been made in TC intensity estimation research, there is currently a lack of research on TC size reflecting TC influence radius. Therefore, we propose a lightweight multi-task learning model (TC-MTLNet) with adaptive loss balance to simultaneously estimate TC intensity and size. Adaptive loss balance is utilized to solve the problem of inconsistent convergence speed of TC intensity and size estimation tasks. The model based on four 2-D convolutions, four 3-D convolutions and three fully connected layers takes up less computational and storage space and improves the accuracy of TC intensity and size estimation by sharing knowledge among multiple tasks. In addition, due to the imbalanced distribution of TC samples, with significantly few low-intensity and high-intensity TC satellite data, this phenomenon poses a great challenge to TC intensity and size estimation. So, we utilize the influence of nearby samples to calibrate the sample density to weight the loss function to enable the model to be generalized to all samples. The result shows that the root-mean-square error (RMSE) of TC intensity estimation is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{8.40}\,\text{kts}$</tex-math></inline-formula> , which is 33.5% lower than that of the Advanced Dvorak Technique (ADT) and 11.4% lower than that of the deep learning method (3DAttentionTCNet). The mean absolute error (MAE) of the TC size estimation is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{20.89}\,\text{nmi}$</tex-math></inline-formula> , which is a 16% reduction compared to the Multi-Platform Tropical Cyclone Surface Winds Analysis (MTCSWA).

A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE)

Using a compilation of Hα fluxes for 384 star-forming galaxies detected during the Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE), we study several important scaling relations linking the star formation rate, specific star formation rate, stellar mass, stellar mass surface density, and atomic gas depletion timescale for a complete sample of galaxies in a rich environment. The extraordinary sensitivity of the narrow-band imaging data allows us to sample the whole dynamic range of the Hα luminosity function, from massive galaxies (Mstar ≃ 1011 M⊙) to dwarf systems (Mstar ≃ 106 M⊙), where the ionised gas emission is due to the emission of single O-early B stars. This extends previous works to a dynamic range in stellar mass and star formation rate (10−4 ≲ SFR ≲ 10 M⊙ yr−1) that has never been explored so far. The main-sequence relation derived for all star-forming galaxies within one virial radius of the Virgo cluster has a slope comparable to that observed in other nearby samples of isolated objects, but its dispersion is about three times larger (∼1 dex). The dispersion is tightly connected to the available amount of HI gas, with gas-poor systems located far below objects of similar stellar mass, but with a normal HI content. When measured on unperturbed galaxies with a normal HI gas content (HI-def ≤ 0.4), the relation has a slope a = 0.92 ± 0.06, an intercept b = −1.57 ± 0.06 (at a pivot point of log Mstar = 8.451 M⊙), and a scatter σ ≃ 0.40, and it has a constant slope in the stellar mass range 106 ≲ Mstar ≲ 3 × 1011 M⊙. The specific star formation rate of HI-poor galaxies is significantly lower than that of HI-rich systems of similar stellar mass, while their atomic gas consumption timescale τHI is fairly similar, in particular, for objects of stellar mass 107 ≲ Mstar ≲ 109 M⊙. We compare these observational results to the prediction of models expressly tuned to reproduce the effects induced by the interaction of galaxies with their surrounding environment. The observed scatter in the main-sequence relation can be reproduced only after a violent and active stripping process such as ram-pressure stripping that removes gas from the disc (outer parts first) and quenches star formation on short (&lt; 1 Gyr) timescales. This rules out milder processes such as starvation. This interpretation is also consistent with the position of galaxies of different star formation activity and gas content within the phase-space diagram. We also show that the star-forming regions that formed in the stripped material outside perturbed galaxies are located well above the main-sequence relation drawn by unperturbed systems. These extraplanar HII regions, which might be at the origin of ultra-compact dwarf galaxies (UCDs) and other compact sources typical in rich environments, are living a starburst phase lasting only ≲50 Myr. They later become quiescent systems.

Finding Peas in the Early Universe with JWST

The Early Release Observations (EROs) of JWST beautifully demonstrate the promise of JWST in characterizing the universe at Cosmic Dawn. We analyze the Near Infrared Spectrograph ERO spectra of three z ∼ 8 galaxies to determine their metallicities, gas temperatures, and ionization. These galaxies offer the first opportunity to understand the physical properties of Epoch-of-Reionization galaxies through detailed rest-optical emission-line spectroscopy. We show that these objects have metal abundances 6.9–8.3, based on both the T e method and on a recent calibration of the R 23 metallicity indicator. Since the spectra are some of the earliest science data from JWST, we compare several line ratios with values expected from robust physics, to validate our measurement procedures. We compare the abundances and emission-line ratios to a nearby sample of Green Pea galaxies—a population of nearby emission-line galaxies whose UV properties resemble Epoch-of-Reionization galaxies, and which often have large Lyman continuum escape fractions. The JWST data show striking further similarities between these high-redshift galaxies and nearby Green Peas. The z ∼ 8 galaxies span the metallicity range covered by Green Peas. They also show the compact morphology that is typical of emission-line-dominated galaxies at all redshifts. Based on these similarities with Green Peas, it is likely that these are the first rest-optical spectra of galaxies that are actively driving cosmological reionization.

Metagenomic insights into the functions of microbial communities in sulfur-rich sediment of a shallow-water hydrothermal vent off Kueishan Island.

Hydrothermal vent (HTV) systems are important habitats for understanding the biological processes of extremophiles on Earth and their relative contributions to material and energy cycles in the ocean. Current understanding on hydrothermal systems have been primarily focused on deep-sea HTVs, and little is known about the functions and metabolisms of microorganisms in shallow-water HTVs (SW-HTVs), which are distinguished from deep-sea HTVs by a depth limit of 200 m. In this study, we analyzed metagenomes of sulfur-rich sediment samples collected from a SW-HTV of Kueishan Island, located in a marginal sea of the western Pacific Ocean. Comparing with a previously published report of pelagic samples from the nearby sampling site, microbial communities in the SW-HTV sediments enriching with genes of both aerobic and anaerobic respiration inferred variable environments in the tested sediments. Abundant genes of energy metabolism encoding sulfur oxidation, H2 oxidation, and carbon fixation were detected from the sediment samples. Sixty-eight metagenome-assembled-genomes (MAGs) were reconstructed to further understand the metabolism and potential interactions between different microbial taxa in the SW-HTVs sediment. MAGs with the highest abundant were chemolithotrophic sulfur-oxidization bacteria, including Sulfurovum represented Campylobacteria involved sox multienzyme, sulfide oxidation genes and rTCA cycle, and Gammaproteobacteria involved dsr gene and CBB cycle. In addition, Desulfobacterota with the potential to participate in sulfur-disproportionating processes also had higher abundance than the sample's overall mean value. The interaction of these bacterial groups allows the microbial communities to efficiently metabolize a large variety of sulfur compounds. In addition, the potential to use simple organic carbon, such as acetate, was found in chemolithotrophic Campylobacterial MAGs. Collectively, our results revealed the complexity of environmental conditions of the vent sediment and highlight the interactive relationships of the dominant microbial populations in driving sulfur cycles in the SW-HTV sediments off Kueishan Island.

Geospatial modeling and mapping of soil organic carbon and texture from spectroradiometric data in Nile basin

Rapid soil properties monitoring and modeling are crucial for sustainable soil management interventions. The analysis of soil properties in the laboratory using standard procedures is time-consuming and requires the use of chemicals. The research aimed to develop a regression model based on the visible-near-infrared-short-wave infrared (Vis-NIR-SWIR, 0.35–2.5 μm) spectral reflectance of soils in Aba Gerima (425 ha), a highland of the Ethiopian Nile Basin. Soil samples were obtained at a 0–20 cm depth from 101 plots in the study area and analyzed for soil organic carbon (SOC) and texture using standard laboratory procedures. Soil reflectance was measured using a spectroradiometer on the air-dried, ground, and sieved soils. ArcGIS software 10.5 was used to conduct the semi-variogram and geostatistical methods—Ordinary Kriging (OK) and Least Distance Weighting (IDW). Strong degrees of spatial dependence are depicted through semi-variogram models. This outcome illustrates a strong spatial dependence and continuity between nearby soil samples. The prediction accuracy of the model based on the values of coefficient of determination (R2) and root mean square error (RMSE) was explained. For clay content, the R2 and RMSE obtained in the Vis-NIR-SWIR were excellent (0.91) and 2.93. An R2 of 0.85 with an RMSE of 1.96 was obtained in predicting sand. The SOC model established indicated an R2 of 0.94 and an RMSEC of 0.01. The generated maps showed that the soil characteristics' spatial variability was adequate to forecast the values of soil fertility indicators in the research area and nearby regions. The soil maps may assist farmers and policymakers in identifying soil nutrient status, improving soil management strategies, rising crop productivity, and guaranteeing ecological integrity. Moreover, further study is required to corroborate our findings and improve the prediction performance of Vis-NIR-SWIR spectroscopy to help better land management interventions.

Spatial metallicity distribution statistics at ≲100 pc scales in the AMUSING++ nearby galaxy sample

ABSTRACT We analyse the spatial statistics of the 2D gas-phase oxygen abundance distributions in a sample of 219 local galaxies. We introduce a new adaptive binning technique to enhance the signal-to-noise ratio of weak lines, which we use to produce well-filled metallicity maps for these galaxies. We show that the two-point correlation functions computed from the metallicity distributions after removing radial gradients are in most cases well-described by a simple injection–diffusion model. Fitting the data to this model yields the correlation length lcorr, which describes the characteristic interstellar medium (ISM) mixing length-scale. We find typical correlation lengths lcorr ∼ 1 kpc, with a strong correlation between lcorr and stellar mass, star formation rate (SFR), and effective radius, and a weak correlation with Hubble type. Two galaxies in the sample show significantly larger lcorr, and both prove to be interacting or merging systems. We show that the trend of lcorr with SFR can be reproduced by a simple transport + feedback model of ISM turbulence at high SFR, and plausibly also at low SFR if dwarf galaxy winds have large mass-loading factors. We also report the first measurements of the injection width that describes the initial radii over which supernova remnants deposit metals. Inside this radius the metallicity correlation function is not purely the product of a competition between injection and diffusion. We show that this size scale is generally smaller than 60 pc.

How Bar Fraction Depends on Baryon Fraction

We use Galaxy Zoo 2 morphologies (based on SDSS Legacy Imaging) alongside the completed Arecibo Legacy Fast ALFA survey measuring H i to investigate the correlation between bar fraction and an estimate of baryonic mass fraction for a sample of nearby (z < 0.06) disk galaxies (M ⋆ > 109 M ⊙). As expected from theoretical considerations, and as seen in previous work using different samples/morphological classifications, we find clear evidence of an increase in bar fraction, particularly strong bar fraction in galaxies with larger baryonic mass fractions (i.e., heavier discs for their dark matter halo).

Down-regulation of LINC-ROR, HOXA-AS2 and MEG3 in gastric cancer

Long non-coding RNAs (lncRNAs) have been identified as modulators of gastric carcinogenesis. Evaluation of expression amounts of these transcripts is a primary but essential step for recognition of the role of lncRNAs in the carcinogenesis. Therefore, we compared expressions of LINC-ROR, HOXA-AS2, MEG3 and HOTTIP lncRNAs in gastric cancer samples and nearby non-cancerous samples. Expression levels of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs have been lower in gastric cancer samples compared with nearby non-cancerous samples (Expression ratios = 0.26, 0.37 and 0.36; P values = 0.021, 0.015 and 0.032, respectively). However, expression levels of HOTTIP were not significantly different between gastric cancer tissues and nearby tissues (P value = 0.43). HOTTIP expression was associated with tumor size (P value = 0.04). In addition, MEG3 expression was associated with site of primary tumor (P = 0.0003). Expressions of LINC-ROR and HOXA-AS2 were not associated with any clinical or pathological parameter. ROC curve analysis revealed that HOXA-AS2 and LINC-ROR could significantly differentiate between gastric cancer samples and nearby non-cancerous tissues (AUC values = 0.68 and 0.64; P values = 0.01 and 0.04, respectively). Taken together, the current investigation provides clues for contribution of LINC-ROR, HOXA-AS2 and MEG3 lncRNAs in gastric carcinogenesis and warrants further mechanistical assays.

High-resolution near-infrared spectroscopy of globular cluster and field stars toward the Galactic bulge

Globular clusters (GCs) play an important role in the formation and evolution of the Milky Way. New candidates are continuously found, particularly in the high-extinction low-latitude regions of the bulge, although their existence and properties have yet to be verified. In order to investigate the new GC candidates, we performed high-resolution near-infrared spectroscopy of stars toward the Galactic bulge using the Immersion Grating Infrared Spectrometer (IGRINS) instrument at the Gemini-South telescope. We selected 15 and 10 target stars near Camargo 1103 and Camargo 1106, respectively, which have recently been reported as metal-poor GC candidates in the bulge. In contrast to the classical approaches used in optical spectroscopy, we determined stellar parameters from a combination of line-depth ratios and the equivalent width of a CO line. The stellar parameters of the stars follow the common trends of nearby APOGEE sample stars in a similar magnitude range. We also determined the abundances of Fe, Na, Mg, Al, Si, S, K, Ca, Ti, Cr, Ni, and Ce through spectrum synthesis. There is no clear evidence of a grouping in radial velocity – metallicity space that would indicate the characterization of either object as metal-poor GCs. This result emphasizes the necessity of follow-up spectroscopy for new GC candidates toward the bulge, although we cannot completely rule out a low probability that we only observed nonmember stars. We also note discrepancies between the abundances of Al, Ca, and Ti when derived from the H- versus the K-band spectra. Although the cause of this discrepancy is not clear, the effects of atmosphere parameters or nonlocal thermodynamic equilibrium are discussed. Our approach and results demonstrate that IGRINS spectroscopy is a useful tool for studying the chemical properties of stars toward the Galactic bulge with a statistical uncertainty in [Fe/H] of ∼0.03 dex, while the systematic error through uncertainties of atmospheric parameter determination, at ∼0.14 dex, is slightly larger than in measurements from optical spectroscopy.

Increased contribution to PM2.5 from traffic-influenced road dust in Shanghai over recent years and predictable future

Traffic contributes to fine particulate matter (PM2.5) in the atmosphere through engine exhaust emissions and road dust generation. However, the evolution of traffic related PM2.5 emission over recent years remains unclear, especially when various efforts to reduce emission e.g., aftertreatment technologies and high emission standards from China IV to China V, have been implemented. In this study, hourly elemental carbon (EC), a marker of primary engine exhaust emissions, and trace element of calcium (Ca), a marker of road dust, were measured at a nearby highway sampling site in Shanghai from 2016 to 2019. A random forest-based machine learning algorithm was applied to decouple the influences of meteorological variables on the measured EC and Ca, revealing the deweathered trend in exhaust emissions and road dust. After meteorological normalization, we showed that non-exhaust emissions, i.e., road dust from traffic, increased their fractional contribution to PM2.5 over recent years. In particular, road dust was found to be more important, as revealed by the deweathered trend of Ca fraction in PM2.5, increasing at 6.1% year−1, more than twice that of EC (2.9% year−1). This study suggests that while various efforts have been successful in reducing vehicular exhaust emissions, road dust will not abate at a similar rate. The results of this study provide insights into the trend of traffic-related emissions over recent years based on high temporal resolution monitoring data, with important implications for policymaking.

Dual recognition strategy for ultra-sensitive fluorescent detection of Hg2+ at femto-molar level based on aptamer functionalized sulfur quantum dots

In the present study, a dual recognition strategy for ultrasensitive detection of Hg 2+ was successfully developed for the first time based on aptamer functionalized sulfur quantum dots (Apt-SQDs). The developed Apt-SQDs not only retained the good fluorescence properties of quantum dots but also overcame the problem of poor selectivity of SQDs for heavy metal ions. This system used the dual recognition strategy, including the combination of S x 2− and Hg 2+ and T-Hg 2+ -T structures to excellently identify and capture Hg 2+ , and an ultrahigh sensitivity fluorescent aptasensor was fabricated. The fluorescent aptasensor had a good response to Hg 2+ at concentrations ranging of 10 −15 to 10 −7 M with an ultralow limit of detection of 0.3 fM, and the response to other metal ions was far less than that to Hg 2+ . It was successfully applied to detect Hg 2+ in nearby environmental water samples (tap water, lake water and river water) with a good recovery rate. Moreover, portable test papers that would be useful for Hg 2+ monitoring in environmental water were designed. The dual recognition strategy not only achieves ultrasensitive fluorescent detection of Hg 2+ but also provides a new insight into the further expansion of the application of SQDs.

New Advances in Diabetes Care Group Education Research

Objective: This study aims to evaluate the effectiveness of health education in diabetes.&#x0D; Methods: This study investigated 40 diabetic patients treated in a tertiary A hospital from February 2022 to May 2022 through convenient nearby sampling. Eligible patients were evaluated by the general basic information questionnaire, diabetes self-behavior management scale through the number of lectures.&#x0D; Results: Through the questionnaire survey, it was found that the average self-behavior management score of patients who have been in diabetes care group was 63.3, which was much higher than the 41.3 points for patients without group education. And patients participating in diabetes group education scored higher than those who did not participate in group education in terms of diet, exercise, and foot care.&#x0D; Conclusion: Group education can improve the self-behavior management ability of diabetic patients.

Transcriptome-proteome compendium of the Antarctic krill (Euphausia superba): Metabolic potential and repertoire of hydrolytic enzymes.

The Antarctic krill (Euphausia superba Dana) is a keystone species in the Southern Ocean that uses an arsenal of hydrolases for biomacromolecule decomposition to effectively digest its omnivorous diet. The present study builds on a hybrid-assembled transcriptome (13,671 ORFs) combined with comprehensive proteome profiling. The analysis of individual krill compartments allowed detection of significantly more different proteins compared to that of the entire animal (1464 vs. 294 proteins). The nearby krill sampling stations in the Bransfield Strait (Antarctic Peninsula) yielded rather uniform proteome datasets. Proteins related to energy production and lipid degradation were particularly abundant in the abdomen, agreeing with the high energy demand of muscle tissue. A total of 378 different biomacromolecule hydrolysing enzymes were detected, including 250 proteases, 99 CAZymes, 14 nucleases and 15 lipases. The large repertoire in proteases is in accord with the protein-rich diet affiliated with E. superba's omnivorous lifestyle and complex biology. The richness in chitin-degrading enzymes allows not only digestion of zooplankton diet, but also the utilisation of the discharged exoskeleton after moulting.

GaiaHub: A Method for Combining Data from the Gaia and Hubble Space Telescopes to Derive Improved Proper Motions for Faint Stars

We present GaiaHub, a publicly available tool that combines Gaia measurements with Hubble Space Telescope (HST) archival images to derive proper motions (PMs). It increases the scientific impact of both observatories beyond their individual capabilities. Gaia provides PMs across the whole sky, but the limited mirror size and time baseline restrict the best PM performance to relatively bright stars. HST can measure accurate PMs for much fainter stars over a small field, but this requires two epochs of observation, which are not always available. GaiaHub yields considerably improved PM accuracy compared to Gaia-only measurements, especially for faint sources (G ≳ 18), requiring only a single epoch of HST data observed more than ∼7 yr ago (before 2012). This provides considerable scientific value, especially for dynamical studies of stellar systems or structures in and beyond the Milky Way (MW) halo, for which the member stars are generally faint. To illustrate the capabilities and demonstrate the accuracy of GaiaHub, we apply it to samples of MW globular clusters (GCs) and classical dwarf spheroidal (dSph) satellite galaxies. This allows us, e.g., to measure the velocity dispersions in the plane of the sky for objects out to and beyond ∼100 kpc. We find, on average, mild radial velocity anisotropy in GCs, consistent with existing results for more nearby samples. We observe a correlation between the internal kinematics of the clusters and their ellipticity, with more isotropic clusters being, on average, more round. Our results also support previous findings that Draco and Sculptor dSph galaxies appear to be radially anisotropic systems.

Charge Exchange X-Ray Emission Detected in Multiple Shells of Supernova Remnant G296.1–0.5

Recent high-resolution X-ray spectroscopy revealed the possible presence of charge exchange (CX) X-ray emission in supernova remnants (SNRs). Although CX is expected to take place at the outermost edges of SNR shells, no significant measurement has been reported so far due to the lack of nearby SNR samples. Here we present an X-ray study of SNR G296.1−0.5, which has a complicated multiple-shell structure, with the Reflection Grating Spectrometer on board XMM-Newton. We select two shells in different regions and find that in both regions the O vii line shows a high forbidden-to-resonance (f/r) ratio that cannot be reproduced by a simple thermal model. Our spectral analysis suggests a presence of CX and the result is also supported by our new radio observation, where we discover evidence of molecular clouds associated with these shells. Assuming G296.1−0.5 has a spherical shock, we estimate that CX is dominant in a thin layer with a thickness of 0.2%–0.3% of the shock radius. The result is consistent with a previous theoretical expectation and we therefore conclude that CX occurs in G296.1−0.5.

Extragalactic fast X-ray transient candidates discovered byChandra(2000–2014)

Context.Extragalactic fast X-ray transients (FXRTs) are short flashes of X-ray photons of unknown origin that last a few seconds to hours.Aims.Our ignorance about their physical mechanisms and progenitor systems is due in part to the lack of clear multiwavelength counterparts in most cases, because FXRTs have only been identified serendipitously.Methods.We develop a systematic search for FXRTs in theChandraSource Catalog (Data Release 2.0; 169.6 Ms over 592.4 deg2, using only observations with |b|&gt; 10° and before 2015), using a straightforward X-ray flare search algorithm and incorporating various multiwavelength constraints to rule out Galactic contamination and characterize the candidates.Results.We report the detection of 14 FXRT candidates from a parent sample of 214 701 sources. Candidates have peak 0.5–7 keV fluxes between 1 × 10−13and 2 × 10−10erg cm−2s−1andT90values from 4 to 48 ks. The sample can be subdivided into two groups: six “nearby” FXRTs that occurred withind ≲ 100 Mpc and eight “distant” FXRTs with likely redshifts ≳0.1. Three distant FXRT candidates exhibit light curves with a plateau (≈1–3 ks duration) followed by a power-law decay and X-ray spectral softening, similar to what was observed for the previously reported FXRT CDF-S XT2, a proposed magnetar-powered binary neutron star merger event. After applying completeness corrections, we calculate event rates for the nearby and distant samples of 53.7−15.1+22.6and 28.2−6.9+9.8deg−2yr−1, respectively.Conclusions.This novel sample ofChandra-detected extragalactic FXRT candidates, although modest in size, breaks new ground in terms of characterizing the diverse properties, nature, and possible progenitors of these enigmatic events.

Adaptive graph regularized and label embedded dictionary learning for pattern classification

During the last decade, graph regularized dictionary learning (DL) models have obtained a lot of attention due to their flexible and discriminative ability in nonlinear pattern classification. However, the conventional graph-regularized methods construct a fixed affinity matrix for nearby samples in high-dimensional data space, which is vulnerable to noisy and redundant sample features. Furthermore, the discrimination of the graph regularized representation is not fully explored with the supervised classifier learning framework. To remedy these limitations, we propose an adaptive graph-regularized and label-embedded DL model for pattern classification. Especially, the affinity graph construction in low-dimensional representation space and the discriminative sparse representation is simultaneously learned in a unified framework for mutual promotion. More concretely, we iteratively update the sample similarity weight matrix in representation space to enhance the model robustness and further impose a supervised label-embedding term on sparse representation to enhancing its discriminative capability for classification. The dictionary orthonormal constraint is also considered to eliminate the redundant atoms and enhance the model discrimination. An efficient alternating direction solution with guaranteed convergence is proposed for the nonconvex and unsmooth model. Experimental results on five benchmark datasets verify the effectiveness of the proposed model.

Evaluation of polluted urban river water quality: a case study of the Xunsi River watershed, China.

Urbanization has caused severe negative impacts on intra-urban river water bodies. In this paper, 22 physicochemical parameters were measured at 20 locations in the level period and 29 locations in the wet and dry period using nearby urban area surface water samples from a medium-sized polluted river (a lake-river continuum, i.e., Xunsi River, Qingling River and Tangxun Lake) in the Yangtze River watershed in Wuhan, China. In this study, the environmental quality standards for surface water of China (GB3838-2002) and the WHO drinking water standard (2011) were used as reference standards. After the basic physicochemical parameters were evaluated, the possible sources of pollution in the study area under urban pressure were examined by applying multivariate statistical techniques. The factor analysis method and entropy weighting method identified three critical factors describing the type of surface water pollution in the watershed: (1) pollution from nutrients, mainly nitrogen pollution, (2) trace metals pollution, mainly from Fe and Pb, and (3) organic pollution. Based on multivariate analysis, the weighted arithmetic water quality index (WQI) was used to assess the water quality of each period. The WQI results show that the water near the urban area of the watershed temporally shows light to moderate pollution overall in the level and wet periods, and sever pollution in the dry period. The worst water quality indicating water that is not suitable for any practical use occurs in the dry period. Spatially distributed in clusters and strips visualized in kriging method, the water quality of the lake source and upstream is better than that of the downstream. In addition, Escherichia coli was detected at consistently high levels, and although the river is not a source of drinking water, it could pose a threat to downstream waterworks.