Explore the vast range of titles available.

The fabrication of Mg–Al layered double hydroxides (LDHs) intercalated with NO 3 − , NO 2 − and p -aminobenzoate and comparative investigation on their chloride absorption in aqueous solutions have been performed in this paper. The Mg–Al-LDHs intercalated with NO 2 − and p -aminobenzoate were synthesized by anion exchange in the host materials, Mg–Al–NO 3 LDH, which was prepared by a coprecipitation method. Also, the equilibrium isotherms of chloride adsorption on the as-obtained LDHs were measured. Besides, their morphologies and microstructures were observed using scanning electron microscopy, X-ray diffraction instrument, infrared spectroscopy and thermal analysis. It has been found that the as-obtained LDHs are well crystallized and have a very large distribution of size. The basal spacing of hexagonal plates is dependent on the types of intercalated anions. Langmuir isotherm is more suitable to fit the experimental data of chloride adsorption than Freundlich isotherm. Compared to Mg–Al–NO 3 LDH, the maximum adsorption capacity of chloride is slightly reduced for Mg–Al–NO 2 LDH, but remarkably decreased for the LDH intercalated with p -aminobenzoate anions.

The Beijing-Arizona Sky Survey (BASS) is a wide-field two-band photometric survey of the northern Galactic Cap using the 90Prime imager on the 2.3 m Bok telescope at Kitt Peak. It is a four-year collaboration between the National Astronomical Observatory of China and Steward Observatory, the University of Arizona, serving as one of the three imaging surveys to provide photometric input catalogs for target selection of the Dark Energy Spectroscopic Instrument (DESI) project. BASS will take up to 240 dark/gray nights to cover an area of about 5400 deg2 in the g and r bands. The 5 limiting AB magnitudes for point sources in the two bands, corrected for the Galactic extinction, are 24.0 and 23.4 mag, respectively. BASS, together with other DESI imaging surveys, will provide unique science opportunities that cover a wide range of topics in both Galactic and extragalactic astronomy.

Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots in the field of remote sensing. Because HSI’s features of integrating image and spectrum provide a considerable data basis for abnormal object detection, HSI-AD has a huge application potential in HSI analysis. It is difficult to effectively extract a large number of nonlinear features contained in HSI data using traditional machine learning methods, and deep learning has incomparable advantages in the extraction of nonlinear features. Therefore, deep learning has been widely used in HSI-AD and has shown excellent performance. This review systematically summarizes the related reference of HSI-AD based on deep learning and classifies the corresponding methods into performance comparisons. Specifically, we first introduce the characteristics of HSI-AD and the challenges faced by traditional methods and introduce the advantages of deep learning in dealing with these problems. Then, we systematically review and classify the corresponding methods of HSI-AD. Finally, the performance of the HSI-AD method based on deep learning is compared on several mainstream data sets, and the existing challenges are summarized. The main purpose of this article is to give a more comprehensive overview of the HSI-AD method to provide a reference for future research work.

As a broad-spectrum antibiotic, tetracycline has become a potential ecological hazard. Herein, titanium nitride (TiN), with an advantageous structure, was synthesized by simple heating rate regulation and constructed for tetracycline hydrochloride (TC-HCl) degradation under light irradiation. All the samples were characterized by X-ray diffraction (XRD), N 2 -adsorption/desorption isotherm, ultraviolet–visible diffuse reflectometry (DRS), scanning electron microscopy (SEM), and electrochemical impedance spectroscopy (EIS). The results showed that the as-prepared TiN- x catalysts exhibited obviously enhanced photocatalytic property toward TC-HCl degradation compared with the commercial pure phase TiN (p-TiN). According to the results of photocatalytic degradation, TiN synthesized at 6 °C/min heating rate had the best removal rate of TC-HCl (90%) after dark reaction for 10 min and photo-degradation for 90 min. In addition, the trapping experiments have demonstrated that the photogenerated holes (h + ) and superoxide radical ( · O 2 - ) are the main oxidation products of the present system. Strikingly, the reuse experiments showed high stability of TiN.

In the standard cosmological model, dark matter drives the structure formation of galaxies and constructs potential wells within which galaxies may form. The baryon fraction in dark halos can reach the Universal value (15.7%) in massive clusters and decreases rapidly as the mass of the system decreases 1 , 2 . The formation of dwarf galaxies is sensitive both to baryonic processes and the properties of dark matter owing to the shallow potential wells in which they form. In dwarf galaxies in the Local Group, dark matter dominates the mass content even within their optical-light half-radii ( r e  ≈ 1 kpc) 3 , 4 . However, recently it has been argued that not all dwarf galaxies are dominated by dark matter 5 – 7 . Here we report 19 dwarf galaxies that could consist mainly of baryons up to radii well beyond r e , at which point they are expected to be dominated by dark matter. Of these, 14 are isolated dwarf galaxies, free from the influence of nearby bright galaxies and high-density environments. This result provides observational evidence that could challenge the formation theory of low-mass galaxies within the framework of standard cosmology. Further observations, in particular deep imaging and spatially resolved kinematics, are needed to constrain the baryon fraction better in such galaxies. Nineteen dwarf galaxies from the ALFALFA catalogue support previous observations of dwarf galaxies that suggested a deficiency in dark matter, challenging the formation theory of low-mass galaxies within the standard cold dark matter model.

GN-z11 was photometrically selected as a luminous star-forming galaxy candidate at redshift z  > 10 on the basis of Hubble Space Telescope imaging data 1 . Follow-up Hubble Space Telescope near-infrared grism observations detected a continuum break that was explained as the Lyα break corresponding to z = 11.0 9 − 0.12 + 0.08 (ref. 2 ). However, its accurate redshift remained unclear. Here we report a probable detection of three ultraviolet emission lines from GN-z11, which can be interpreted as the [C  iii ] λ1907, C  iii ] λ1909 doublet and O  iii ] λ1666 at z  = 10.957 ± 0.001 (when the Universe was only ~420 Myr old, or ~3% of its current age). This is consistent with the redshift of the previous grism observations, supporting GN-z11 as the most distant galaxy known to date. Its ultraviolet lines probably originate from dense ionized gas that is rarely seen at low redshifts, and its strong [C  iii ] and C  iii ] emission is partly due to an active galactic nucleus or enhanced carbon abundance. GN-z11 is luminous and young, yet moderately massive, implying a rapid build-up of stellar mass in the past. Future facilities will be able to find the progenitors of such galaxies at higher redshift and probe the cosmic epoch at the beginning of reionization. The detection of three ultraviolet emission lines from GN-z11 can be interpreted as the [C  iii ] λ1907, C  iii ] λ1909 doublet and O  iii ] λ1666 at z  = 10.957 ± 0.001, confirming GN-z11 as the most distant galaxy known to date and revealing the properties of its dense ionized gas.

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA’s subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.

The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.

The intestinal flora is correlated with the occurrence of colorectal cancer. We evaluate a new predictive model for the non-invasive diagnosis of colorectal cancer based on intestinal flora to verify the clinical application prospects of the intestinal flora as a new biomarker in non-invasive screening of colorectal cancer. Subjects from two independent Asian cohorts (cohort I, consisting of 206 colorectal cancer and 112 healthy subjects; cohort II, consisting of 67 colorectal cancer and 54 healthy subjects) were included. A probe-based duplex quantitative PCR (qPCR) determination was established for the quantitative determination of candidate bacterial markers. We screened through the gutMEGA database to identify potential non-invasive biomarkers for colorectal cancer, including ( ), ( ), ( ), ( ), and ( ). A predictive model with good sensitivity and specificity was established as a new diagnostic tool for colorectal cancer. Under the best cutoff value that maximizes the sum of sensitivity and specificity, and had better specificity and sensitivity than other target bacteria. The combined detection model of five kinds of bacteria showed better diagnostic ability than or alone (AUC = 0.861, < 0.001). These findings were further confirmed in the independent cohort II. Particularly, the combination of bacterial markers and fecal immunochemical test (FIT) improved the diagnostic ability of the five bacteria (sensitivity 67.96%, specificity 89.29%) for patients with colorectal cancer. Fecal-based colorectal cancer-related bacteria can be used as new non-invasive diagnostic biomarkers of colorectal cancer. Simultaneously, the molecular biomarkers in fecal samples are similar to FIT, have the applicability in combination with other detection methods, which is expected to improve the sensitivity of diagnosis for colorectal cancer, and have a promising prospect of clinical application.

Background Similar to humans, the zebrafish brain plays a central role in regulating sexual reproduction, maturation and sexual behavior. However, systematic studies of the dimorphic patterns of gene expression in the brain of male and female zebrafish are lacking. Results In this study, the mRNA and lncRNA expression profiles were obtained from the brain tissue samples of the three male and three female zebrafish by high-throughput transcriptome sequencing. We identified a total of 108 mRNAs and 50 lncRNAs with sex-based differential expression. We randomly selected four differentially expressed genes for RT-qPCR verification and the results certified that the expression pattern showed a similar trend between RNA-seq and RT-qPCR results. Protein-protein interaction network analysis, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to obtain the biological significance of differentially expressed mRNA in the brain dimorphism of zebrafish. Finally, a Pearson correlation analysis was performed to construct the co-expression network of the mRNAs and lncRNAs. Conclusions We found that 12 new lncRNAs not only have significant gender specificity in the brain of zebrafish, and this finding may provide a clue to further study of the functional difference between male and female zebrafish brain.