Variations in Soil Bacterial Community Diversity and Structures Among Different Revegetation Types in the Baishilazi Nature Reserve

We compared patterns of soil bacterial community diversity and structure in six secondary forests (JM, Juglans mandshurica; QM, Quercus mongolica; MB, mixed Broadleaf forest; BE, Betula ermanii; CB, conifer-broadleaf forest; PT, Pinus tabuliformis) and two plantation forests (LG, Larix gmelinii; PK, Pinus koraiensis) of the Baishilazi Nature Reserve, China, based on the 16S rRNA high-throughput Illumina sequencing data. The correlations between the bacterial community and soil environmental factors were also examined. The results showed that the broadleaf forests (JM, QM, MB) had higher levels of total C (TC), total N (TN), available N (AN), and available K (AK) compared to the coniferous forests (PT, LG, PK) and conifer-broadleaf forest (CB). Different revegetation pathways had different effects on the soil bacterial community diversity and structure. For the α-diversity, the highest Shannon index and Simpson index were found in JM. The Simpson index was significantly positively correlated with the available P (AP) (P < 0.05), and the Shannon index was significantly positively correlated with AK (P < 0.05). Compared with others, the increased ACE index and Chao1 index were observed in the CB and MB, and both of these α-diversity were significantly negative with AK (P < 0.05). The relative abundances of bacterial phyla and genera differed among different revegetation types. At the phylum level, the dominant phylum groups in all soils were Proteobacteria, Acidobacteria, Actinobacteria, Verrucomicrobia, Chloroflexi, Bacteroidetes, Gemmatimonadetes, and Planctomycetes. Significant differences in relative abundance of bacteria phyla were found for Acidobacteria, Actinobacteria, Chloroflexi, Gemmatimonadetes, and Proteobacteria. Correlation analysis showed that Soil pH, TC, TN, AP, and AK were the main abiotic factors structuring the bacterial communities. As revealed by the clear differentiation of bacterial communities and the clustering in the heatmap and in the PCA plots, broadleaf forests and coniferous forests harbored distinct bacterial communities, indicating a significant impact of the respective reforestation pathway on soil bacterial communities in the Baishilazi Nature Reserve

Magnetic Mn5Ge3 nanocrystals embedded in crystalline Ge: a magnet/semiconductor hybrid synthesized by ion implantation

The integration of ferromagnetic Mn5Ge3 with the Ge matrix is promising for spin injection in a silicon-compatible geometry. In this paper, we report the preparation of magnetic Mn5Ge3 nanocrystals embedded inside the Ge matrix by Mn ions implantation at elevated temperature. By X-ray diffraction and transmission electron microscopy, we observe crystalline Mn5Ge3 with variable size depending on the Mn ion fluence. The electronic structure of Mn in Mn5Ge3 nanocrystals is 3d6 configuration, the same as in bulk Mn5Ge3. A large positive magnetoresistance has been observed at low temperatures. It can be explained by the conductivity inhomogeneity in the magnetic/semiconductor hybrid system.Comment: 16 pages, 5 figure

Different revegetation types alter soil physical-chemical characteristics and fungal community in the Baishilazi Nature Reserve

The effects of different revegetation types on soil physical–chemical characteristics and fungal community diversity and composition of soils sampled from five different revegetation types (JM, Juglans mandshurica; QM, Quercus mongolica; conifer-broadleaf forest (CB); LG, Larix gmelinii; PK, Pinus koraiensis) in the Baishilazi Nature Reserve were determined. Soil fungal communities were assessed employing ITS rRNA Illunima Miseq high-throughput sequencing. Responses of the soil fungi community to soil environmental factors were assessed through canonical correspondence analysis (CCA) and Pearson correlation analysis. The coniferous forests (L. gmelinii, P. koraiensis) and CB had reduced soil total carbon (C), total nitrogen (N), and available nitrogen (AN) values compared with the broadleaf forest (J. mandshurica, Q. mongolica). The average fungus diversity according to the Shannon, ACE, Chao1, and Simpson index were increased in the J. mandshurica site. Basidiomycota, Ascomycota, Zygomycota, and Rozellomycota were the dominant fungal taxa in this region. The phylum Basidiomycota was dominant in the Q. mongolica, CB, L. gmelinii, and P. koraiensis sites, while Ascomycota was the dominant phylum in the J. mandshurica site. The clear differentiation of fungal communities and the clustering in the heatmap and in non-metric multidimensional scaling plot showed that broadleaf forests, CB, and coniferous forests harbored different fungal communities. The results of the CCA showed that soil environmental factors, such as soil pH, total C, total N, AN, and available phosphorus (P) greatly influenced the fungal community structure. Based on our results, the different responses of the soil fungal communities to the different revegetation types largely dependent on different forest types and soil physicochemical characteristic in Baishilazi Nature Reserve

ConvKyber: Unleashing the Power of AI Accelerators for Faster Kyber with Novel Iteration-based Approaches

The remarkable performance capabilities of AI accelerators offer promising opportunities for accelerating cryptographic algorithms, particularly in the context of lattice-based cryptography. However, current approaches to leveraging AI accelerators often remain at a rudimentary level of implementation, overlooking the intricate internal mechanisms of these devices. Consequently, a significant number of computational resources is underutilized. In this paper, we present a comprehensive exploration of NVIDIA Tensor Cores and introduce a novel framework tailored specifically for Kyber. Firstly, we propose two innovative approaches that efficiently break down Kyber\u27s NTT into iterative matrix multiplications, resulting in approximately a 75% reduction in costs compared to the state-of-the-art scanning-based methods.Secondly, by reversing the internal mechanisms, we precisely manipulate the internal resources of Tensor Cores using assembly-level code instead of inefficient standard interfaces, eliminating memory accesses and redundant function calls. Finally, building upon our highly optimized NTT, we provide a complete implementation for all parameter sets of Kyber. Our implementation surpasses the state-of-the-art Tensor Core based work, achieving remarkable speed-ups of 1.93x, 1.65x, 1.22x and 3.55x for polyvec_ntt, KeyGen, Enc and Dec in Kyber-1024, respectively. Even when considering execution latency, our throughput-oriented full Kyber implementation maintains an acceptable execution latency. For instance, the execution latency ranges from 1.02 to 5.68 milliseconds for Kyber-1024 on R3080 when achieving the peak throughput

ConvKyber: Unleashing the Power of AI Accelerators for Faster Kyber with Novel Iteration-based Approaches

The remarkable performance capabilities of AI accelerators offer promising opportunities for accelerating cryptographic algorithms, particularly in the context of lattice-based cryptography. However, current approaches to leveraging AI accelerators often remain at a rudimentary level of implementation, overlooking the intricate internal mechanisms of these devices. Consequently, a significant number of computational resources is underutilized. In this paper, we present a comprehensive exploration of NVIDIA Tensor Cores and introduce a novel framework tailored specifically for Kyber. Firstly, we propose two innovative approaches that efficiently break down Kyber’s NTT into iterative matrix multiplications, resulting in approximately a 75% reduction in costs compared to the state-of-the-art scanning-based methods. Secondly, by reversing the internal mechanisms, we precisely manipulate the internal resources of Tensor Cores using assembly-level code instead of inefficient standard interfaces, eliminating memory accesses and redundant function calls. Finally, building upon our highly optimized NTT, we provide a complete implementation for all parameter sets of Kyber. Our implementation surpasses the state-of-the-art Tensor Core based work, achieving remarkable speed-ups of 1.93x, 1.65x, 1.22x and 3.55x for polyvec_ntt, KeyGen, Enc and Dec in Kyber-1024, respectively. Even when considering execution latency, our throughput-oriented full Kyber implementation maintains an acceptable execution latency. For instance, the execution latency ranges from 1.02 to 5.68 milliseconds for Kyber-1024 on R3080 when achieving the peak throughput

Genetic dissection of grain iron concentration in hexaploid wheat (Triticum aestivum L.) using a genome-wide association analysis method

Iron (Fe) is an essential micronutrient of the body. Low concentrations of bioavailable Fe in staple food result in micronutrient malnutrition. Wheat (Triticum aestivum L.) is the most important global food crop and thus has become an important source of iron for people. Breeding nutritious wheat with high grain-Fe content has become an effective means of alleviating malnutrition. Understanding the genetic basis of micronutrient concentration in wheat grains may provide useful information for breeding for high Fe varieties through marker-assisted selection (MAS). Hence, in the present study, genome-wide association studies (GWAS) were conducted for grain Fe. An association panel of 207 accessions was genotyped using a 660K SNP array and phenotyped for grain Fe content at three locations. The genotypic and phenotypic data obtained thus were used for GWAS. A total of 911 SNPs were significantly associated with grain Fe concentrations. These SNPs were distributed on all 21 wheat chromosomes, and each SNP explained 5.79–25.31% of the phenotypic variations. Notably, the two significant SNPs (AX-108912427 and AX-94729264) not only have a more significant effect on grain Fe concentration but also have the reliability under the different environments. Furthermore, candidate genes potentially associated with grain Fe concentration were predicted, and 10 candidate genes were identified. These candidate genes were related to transport, translocation, remobilization, and accumulationof ironin wheat plants. These findings will not only help in better understanding the molecular basis of Fe accumulation in grains, but also provide elite wheat germplasms to develop Fe-rich wheat varieties through breeding