Chromosome-level genome assembly of the Stoliczka's Asian trident bat (Aselliscus stoliczkanus).

Stoliczka's Asian trident bat (Aselliscus stoliczkanus) is a small-bodied species and very sensitive to climate change. Here, we presented a chromosome-level genome assembly of A. stoliczkanus by combining Illumina sequencing, Nanopore sequencing and high-throughput chromatin conformation capture (Hi-C) sequencing technology. The genome assembly was 2.18 Gb in size with 98.26% of the genome sequences anchored onto 14 autosomes and two sex chromosomes (X and Y). The quality of the genome assembly is very high with a contig and scaffold N50 of 72.98 and 162 Mb, respectively, Benchmarking Universal Single-Copy Orthologs (BUSCO) score of 96.6%, and the consensus quality value (QV) of 47.44. A total of 20,567 genes were predicted and 98.8% of these genes were functionally annotated. Syntenic blocks between A. stoliczkanus and Homo sapiens, together with previous comparative cytogenetic studies, provide valuable foundations for further comparative genomic and cytogenetic studies in mammals. The reference-quality genome of A. stoliczkanus contributes an important resource for conservative genomics and landscape genomics in predicting adaptation and vulnerability to climate change.

Rapid degradation of malachite green by CoFe2O4-SiC foam under microwave radiation.

This study demonstrated rapid degradation of malachite green (MG) by a microwave (MW)-induced enhanced catalytic process with CoFe2O4-SiC foam. The catalyst was synthesized from CoFe2O4 particles and SiC foam by the hydrothermal method. X-ray diffraction and scanning electron microscopy techniques were used to confirm that CoFe2O4 particles were settled on the surface of SiC foam. In this experiment, a novel fixed-bed reactor was set up with this catalyst for a continuous flow process in a MW oven. The different parameters that affect the MW-induced degradation rate of MG were explored. The MW irradiation leads to the effective catalytic degradation of MG, achieving 95.01% degradation within 5 min at pH 8.5. At the same time, the good stability and applicability of CoFe2O4-SiC foam for the degradation process were also discussed, as well as the underlying mechanism. In brief, these findings make the CoFe2O4-SiC foam an excellent catalyst that could be used in practical rapid degradation of MG.

Theoretical and experimental study of the mechanisms of phosphate removal in the system containing Fe(III)-ions.

A process-oriented investigation of phosphate removal by ferric salt was carried out in this study. The kinetics of amorphous ferric phosphate (FePO4(s)) formation has been investigated over the pH range of 6.0-8.0 using sulfosalicylic acid as a competitive ligand. The FePO4(s) formation rate constants varied in a narrow range over the pH range examined in this study. And the maximum of (0.90 ± 0.11) × 104 L mol-1 s-1 was obtained at pH 7.5 and the minimum value of (0.05 ± 0.01) × 104 L mol-1 s-1 was obtained at pH 6.0. These values are two orders of magnitude lower than the rate constants for Fe(III) hydrolysis-precipitation, and hence, the extent of FePO4(s) formation when ferric ions are added to aqueous solution is extremely low. Subsequently, the characteristics of the amorphous ferric oxide (AFO) with different ages were investigated, and it was found that the BET surface area, the average pore width, and the charge capacitance were various for different AFO with various ages. Phosphate adsorption by AFO was significantly affected by AFO aging and the manner of adding Fe(III), which was successfully described by a diffuse layer model. By using surface sites concentration obtained, the kinetics constant of AFO aging could be calculated by a functional equation at a certain pH and time. Graphic abstract Description of the precipitation and aging processes for the phosphate removal by Fe(III)-ions.

An effective hybrid approach of gene selection and classification for microarray data based on clustering and particle swarm optimization.

In this paper, a hybrid approach based on clustering and Particle Swarm Optimisation (PSO) is proposed to perform gene selection and classification for microarray data. In the new method, firstly, genes are partitioned into a predetermined number of clusters by K-means method. Since the genes in each cluster have much redundancy, Max-Relevance Min-Redundancy (mRMR) strategy is used to reduce redundancy of the clustered genes. Then, PSO is used to perform further gene selection from the remaining clustered genes. Because of its better generalisation performance with much faster convergence rate than other learning algorithms for neural networks, Extreme Learning Machine (ELM) is chosen to evaluate candidate gene subsets selected by PSO and perform samples classification in this study. The proposed method selects less redundant genes as well as increases prediction accuracy and its efficiency and effectiveness are verified by extensive comparisons with other classical methods on three open microarray data.