Long-term fertilization alters microbial community but fails to reclaim soil organic carbon stocks in a land-use changed soil of the Tibetan Plateau

The microbial community and soil organic carbon (SOC), which play vital roles in soil fertility and the global C cycle, have been heavily altered due to land-use changes and long-term fertilization. However, the effect of long-term fertilization on the microbial community and SOC in land-use changed soil is still unclear. In this study, a 26-year field experiment is conducted to detect the bacterial community and SOC stocks in the soils from meadow grasslands (M), croplands without fertilization (NF), and croplands with fertilization for 13 (F13a) and 26 years (F26a) in the Tibetan Plateau. The results show that land-use change from meadow grassland to cropland induced a decrease in the SOC stocks of total (TOC), free (FOC) and permanganate-oxidizable OC (POxC) by 61.8–85.0, 51.1–82.8, and 78.4–95.8%, respectively. Long-term fertilization increased the SOC stocks by 124.4–419.0%, which was still lower than those in the M soils. In addition, macroaggregates (MAA) and bacterial diversity displayed reductions when the land-use was changed from grassland to cropland, but they were enhanced after long-term fertilization. Land-use change and long-term fertilization both altered the microbial community. MAA served as a habitat for the microbial community and physical protection for SOC. This may be a key driver of changes in the bacterial community and SOC. This study demonstrates that long-term fertilization alters the microbial community but fails to restore SOC stocks to the level of uncropped meadow soils. Long-term fertilization integrated with macroaggregates are required to improve OC sequestration for developing sustainable agriculture and mitigating global climate change.</p

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (<i>Glycine max</i> L.)

Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean

Densification of Genetic Map and Stable Quantitative Trait Locus Analysis for Amino Acid Content of Seed in Soybean (<i>Glycine max</i> L.)

Soybean, a primary vegetable protein source, boasts favorable amino acid profiles; however, its composition still falls short of meeting human nutritional demands. The soybean amino acid content is a quantitative trait controlled by multiple genes. In this study, an F2 population of 186 individual plants derived from the cross between ChangJiangChun2 and JiYu166 served as the mapping population. Based on the previously published genetic map of our lab, we increased the density of the genetic map and constructed a new genetic map containing 518 SSR (simple sequence repeats) markers and 64 InDel (insertion-deletion) markers, with an average distance of 5.27 cm and a total length of 2881.2 cm. The content of eight essential amino acids was evaluated in the F2:5, F2:6, and BLUP (best linear unbiased prediction). A total of 52 QTLs (quantitative trait loci) were identified, and 13 QTL clusters were identified, among which loci02.1 and loci11.1 emerged as stable QTL clusters, exploring candidate genes within these regions. Through GO enrichment and gene annotation, 16 candidate genes associated with soybean essential amino acid content were predicted. This study would lay the foundation for elucidating the regulatory mechanisms of essential amino acid content and contribute to germplasm innovation in soybeans

Transitioning from testbeds to ships: an experience study in deploying the TIPPERS Internet of Things platform to the US Navy

International audienceThis paper describes the collaborative effort between privacy and security researchers at nine different institutions along with researchers at the Naval Information Warfare Center to deploy, test, and demonstrate privacy-preserving technologies in creating sensor-based awareness using the Internet of Things (IoT) aboard naval vessels in the context of the US Navy's Trident Warrior 2019 exercise. Funded by DARPA through the Brandeis program, the team built an integrated IoT data management middleware, entitled TIPPERS, that supports privacy by design and integrates a variety of Privacy Enhancing Technologies (PETs), including differential privacy, computation on encrypted data, and fine-grained policies. We describe the architecture of TIPPERS and its use in creating a smart ship that offers IoT-enabled services such as occupancy analysis, fall detection, detection of unauthorized access to spaces, and other situational awareness scenarios. We describe the privacy implications of creating IoT spaces that collect data that might include individuals' data (e.g., location) and analyze the tradeoff between privacy and utility of the supported PETs in this context