A saturated genetic linkage map of autotetraploid alfalfa (Medicago sativa L.) developed using genotyping-by-sequencing is highly syntenous with the Medicago truncatula genome.

A genetic linkage map is a valuable tool for quantitative trait locus mapping, map-based gene cloning, comparative mapping, and whole-genome assembly. Alfalfa, one of the most important forage crops in the world, is autotetraploid, allogamous, and highly heterozygous, characteristics that have impeded the construction of a high-density linkage map using traditional genetic marker systems. Using genotyping-by-sequencing (GBS), we constructed low-cost, reasonably high-density linkage maps for both maternal and paternal parental genomes of an autotetraploid alfalfa F1 population. The resulting maps contain 3591 single-nucleotide polymorphism markers on 64 linkage groups across both parents, with an average density of one marker per 1.5 and 1.0 cM for the maternal and paternal haplotype maps, respectively. Chromosome assignments were made based on homology of markers to the M. truncatula genome. Four linkage groups representing the four haplotypes of each alfalfa chromosome were assigned to each of the eight Medicago chromosomes in both the maternal and paternal parents. The alfalfa linkage groups were highly syntenous with M. truncatula, and clearly identified the known translocation between Chromosomes 4 and 8. In addition, a small inversion on Chromosome 1 was identified between M. truncatula and M. sativa. GBS enabled us to develop a saturated linkage map for alfalfa that greatly improved genome coverage relative to previous maps and that will facilitate investigation of genome structure. GBS could be used in breeding populations to accelerate molecular breeding in alfalfa

Identification of genes induced by salt stress from Medicago truncatula L. seedlings

In order to identify genes induced during the salt stress response in barrel medic (Medicago truncatula L) seedlings, a cDNA library by salt stress was constructed  by suppression subtractive hybridization (SSH). Total RNA from 15-day-old seedlings was used as a ‘driver’, and total RNA from seedlings induced by salt was used as a ‘tester’. One hundred and sixty nine clones identified as positive clones by reverse northern dot-blotting resulted in 75 uni-ESTs that comprised of 13 contigs  and 62 singletons. Basic Local Alignment Search Tool (BLAST) analysis of deduced protein sequences revealed that 35 expressed sequence tags (ESTs) had identity similar to proteins with known function, while 27 could not be annotated at all. Most of the known function sequences were homologous to genes involved in abiotic stress in plants. Among these protein, citrate synthase, ribulose- 1,5-bisphosphate carboxylase, chloroplast protein, phosphoenolpyruvate carboxylase and  chloroplast outer envelope protein are related to photosynthesis; DNA binding/transcription factor, putative AP2/EREBP transcription factor, Cab9 gene, photosystem II polypeptide and calcium-dependent protein kinase play a significant role in signal transduction and transcription regulation; and aldolase and sucrose synthase are interrelated to osmolyte synthesis. Moreover, 5 of the ESTs, similar to genes from other plant species and closely involved in salt stress were isolated from M. truncatula L. They are superoxide dimutase (SOD)-1, gene for copper/zinc superoxide dismutase, cysteine protease, Na+/H+ antiporter and salt overly sensitive 2 (SOS2). To further assess the expression level of salt-induced ESTs, real-time polymerase chain reaction (PCR) analysis was employed, and the result showed that these genes have significantly increased expression and probably play an important role in the response of plants to salt stress.Key words: Barrel medic (Medicago truncatula L.), suppression subtraction hybridization (SSH), reverse northern dot-blotting, salt stress, real-time polymerase chain reaction (PCR)

Isolation and functional characterization of a Medicago sativa L. gene, MsLEA3-1

A full-length cDNA of 1,728 nt, called MsLEA3-1, was cloned from alfalfa by rapid amplification of cDNA ends from an expressed sequence tag homologous to soybean pGmPM10 (accession No. AAA91965.1). MsLEA3-1, encodes a deduced protein of 436 amino acids, a calculated molecular weight of 47.0 kDa, a theoretical isoelectric point of 5.18, and closest homology with late embryogenesis abundant proteins in soybean. Sequence homology suggested a signal peptide in the N terminus, and subcellular localization with GFP revealed that MsLEA3-1 was localized preferentially to the nucleolus. The transcript titre of MsLEA3-1 was strongly enriched in leaves compared with roots and stems of mature alfalfa plants. Gene expression of MsLEA3-1 was strongly induced when seedlings were treated with NaCl and ABA. Expression of the MsLEA3-1 transgenic was detected in transgenic tobacco. Malondialdehyde content and, electrical conductivity content were reduced and electrical conductivity and proline content were increased in transgenic tobacco compared with non-transgenic tobacco under salt stress. The results showed that accumulation of the MsLEA3-1 protein in the vegetative tissues of transgenic plants enhanced their tolerance to salt stress. These results demonstrate a role for the MsLEA3-1 protein in stress protection and suggest the potential of the MsLEA3-1 gene for genetic engineering of salt tolerance

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

Comparisons of the accuracy and consistency of different remote-sensing land cover datasets are important for the rational application of multi-source land cover datasets to regional development, or to studies of global or local environmental change. Existing comparisons of accuracy or spatial consistency among land cover datasets primarily use confusion or transfer matrices and focus on the type and area consistency of land cover. However, less attention has been paid to the consistency of spatial patterns, and quantitative analyses of spatial pattern consistency are rare. However, when proportions of land cover types are similar, spatial patterns are essential for studies of the ecological functions of a landscape system. In this study, we used classical landscape indices that quantifies spatial patterns to analyze the spatial pattern consistency among different land cover datasets, and chose three datasets (GlobeLand30-2010, FROM-GLC2010, and SERVIR MEKONG2010) in northern Laos as a case study. We also analyzed spatial pattern consistency at different scales after comparing the landscape indices method with the confusion matrix method. We found that the degree of consistency between GlobeLand30-2010 and SERVIR MEKONG2010 was higher than that of GlobeLand30-2010 and FROM-GLC2010, FROM-GLC2010, and SERVIR MEKONG2010 based on the confusion matrix, mainly because of the best forest consistency and then water. However, the spatial consistency results of the landscape indices analysis show that the three datasets have large differences in the number of patches (NP), patch density (PD), and landscape shape index (LSI) at the original scale of 30 m, and decrease with the increase of the scale. Meanwhile, the aggregation index (AI) shows different changes, such as the changing trend of the forest aggregation index increasing with the scale. Our results suggested that, when using or producing land cover datasets, it is necessary not only to ensure the consistency of landscape types and areas, but also to ensure that differences among spatial patterns are minimized, especially those exacerbated by scale. Attention to these factors will avoid larger deviations and even erroneous conclusions from these data products

Mut9p-LIKE KINASE Family Members: New Roles of the Plant-Specific Casein Kinase I in Plant Growth and Development

Casein kinase I (CK1), a ubiquitous serine/threonine (Ser/Thr) protein kinase in eukaryotes, plays pivotal roles in a wide spectrum of cellular functions including metabolism, cell cycle progression, developmental control and stress response. Plant CK1 evolves a lineage expansion, resulting in a unique branch of members exclusive to the kingdom. Among them, Arabidopsis Mut9p-LIKE KINASEs (MLKs) target diverse substrates including histones and the key regulatory proteins involving in physiological processes of light signaling, circadian rhythms, phytohormone and plant defense. Deregulation of the kinase activity by mutating the enzyme or the phosphorylation sites of substrates causes developmental disorders and susceptibility to adverse environmental conditions. Recent findings suggest that MLKs have evolved as a general kinase that modifies transcription factors or primary regulatory proteins in a dynamic way. Here, we summarize the current knowledge of the roles of MLKs and MLK orthologs in several commercially important crops

Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning

Analyzing consistency of different land-cover data is significant to reasonably select land-cover data for regional development and resource survey. Existing consistency analysis of different datasets mainly focused on the phenomena of spatial consistency regional distribution or accuracy comparison to provide guidelines for choosing the land-cover data. However, few studies focused on the hidden inconsistency distribution rules of different datasets, which can provide guidelines not only for users to properly choose them but also for producers to improve their mapping strategies. Here, we zoned the Sindh province of Pakistan by the Terrestrial Ecoregions of the World as a case to analyze the inconsistency patterns of the following three datasets: GlobeLand30, FROM-GLC, and regional land cover (RLC). We found that the inconsistency of the three datasets was relatively low in areas having a dominant type and also showing homogeneity characteristics in remote sensing images. For example, cropland of the three datasets in the ecological zoning of Northwestern thorn scrub forests showed high consistency. In contrast, the inconsistency was high in areas with strong heterogeneity. For example, in the southeast of the Thar desert ecological zone where cropland, grassland, shrubland, and bareland were interleaved and the surface cover complexity was relatively high, the inconsistency of the three datasets was relatively high. We also found that definitions of some types in different classification systems are different, which also increased the inconsistency. For example, the definitions of grassland and bareland in GlobeLand30 and RLC were different, which seriously affects the consistency of these datasets. Hence, producers can use the existing land-cover products as reference in ecological zones with dominant types and strong homogeneity. It is necessary to pay more attention on ecological zoning with complex land types and strong heterogeneity. An effective way is standardizing the definitions of complex land types, such as forest, shrubland, and grassland in these areas

Collaborative Extraction of Paddy Planting Areas with Multi-Source Information Based on Google Earth Engine: A Case Study of Cambodia

High-precision spatial mapping of paddy planting areas is very important for food security risk assessment and agricultural monitoring. Previous studies have mainly been based on multi-source satellite imagery, the fusion of Synthetic Aperture Radar (SAR) with optical data, and the combined use of multi-scale and multi-source sensors. However, there have been few studies on paddy spatial mapping using collaborative multi-source remote sensing product information, especially in tropical regions such as Southeast Asia. Therefore, based on the Google Earth Engine (GEE) platform, in this study, Cambodia, which is dominated by agriculture, was taken as the study area, and an extraction scheme for paddy planting areas was developed from collaborative multi-source information, including multi-source remote sensing images (Sentinel-1 and Sentinel-2), multi-source remote sensing land cover products (GFSAD30SEACE, GLC_FCS30-2015, FROM_GLC2015, SERVIR MEKONG, and GUF), paddy phenology information, and topographic features. Evaluation and analysis of the extraction results and the SERVIR MEKONG and ESACCI-LC paddy products revealed that the accuracy of the paddy planting areas extracted using the proposed method is the highest, with an overall accuracy of 89.90%. The results of the proposed method are better than those of the other products in terms of the outline of the paddy planting areas and the description of the road information. The results of this study provide a reference for future high-precision paddy mapping