Abstract:Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The "woodland" LAI/FPAR is the worst, with a spatial OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 2520 similarity (O) of 58.17% due to the misclassification between "woodland" and "others". The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.

Objective Tuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculosis strains makes the problem more severe. Pyrazinamide (PZA) is an important component for short-course treatment regimens and first- and second-line treatment regimens. This research aims for fast diagnosis of M. tuberculosis resistance to PZA and identification of genetic features causing resistance. Materials and Methods We use clinically collected genomic data of M. tuberculosis that are resistant or susceptible to PZA. A machine learning platform is built to diagnose PZA resistance using the whole genome sequence data, and to identify resistance genes and mutations. The platform consists of a deep convolutional neural network (DCNN) model for resistance diagnosis and a support vector machine (SVM) model as a surrogate to identify resistance genes and mutations. Results The DCNN model achieves a PZA resistance diagnosis accuracy of 93%. Each prediction takes less than a second. The SVM has revealed 2 novel genes, embB and gyrA, besides the well-known pncA gene, and 9 mutations that harbor PZA resistance. Discussion The DCNN and SVM machine learning platform, if used together with the real-time genome sequencing machines, could allow for rapid PZA diagnosis, allowing for critical time to ensure good patient outcomes, and preventing outbreaks of deadly infections. Furthermore, identifying pertinent resistance genes and mutations will help researchers better understand the biological mechanisms behind resistance. Conclusions Machine learning can be used to achieve high-accuracy resistance prediction, and identify genes and mutations causing the resistance.

Dynamic monitoring of vegetation coverage changes, especially on a relatively large temporal scale, have important practical significance in urban planning and environmental protection. The objective of this study is to dynamically investigate the urban landscape patterns of vegetation coverage based on remote sensing techniques. Multi-temporal Landsat images of 1990, 2000 and 2013 were firstly used to produce three vegetation coverage maps of Hefei City, Anhui Province, China with five grades using the NDVI (Normalized Difference Vegetation Index) dimidiate pixel model. Subsequently, a total of eight landscape pattern indictors in FRAGSTATS 4.2 were selected to analyze the dynamic characteristics of area, quantity and density for the study area with different vegetation coverage grades. The results showed that 1) the dominant vegetation coverage of 1990, 2000 and 2013 were the high vegetation coverage, the moderate vegetation coverage and the moderate-to-high vegetation coverage, respectively. The acreage of non-vegetation coverage increased by 1.89%, while the high vegetation coverage decreased by 10.48% from 1990 to 2013; 2) the quantity and density of patches decreased by 33.42% and 33.41% during 1990-2013. Shannon's diversity index and Shannon's evenness index increased from 0.92 in 1990 to 0.97 in 2000, and then declined to 0.96 in 2013; and 3) the contagion index had an upward trend and conversely the aggregation index showed no significant changes, but both of them were close to 1 during 1990-2013. In comparison with natural influences, the primary driving forces causing the changes were ascribed to human factors including the rapid population growth and fast-growing urban areas.

BackgroundGenome‐wide association studies (GWAS) of Alzheimer’s disease (AD) have implicated microglia in AD pathogenesis, and microglia is a promising cellular therapeutic target in AD. However, it remains unclear when and how AD genetic risk localizing to microglia contributes to AD pathophysiology.MethodParticipants are from two community‐based cohorts (Religious Orders Study, the Rush Memory and Aging Project [ROSMAP]; n = 299 deceased; age 88.9±7.1, female 67%) and the screening phase of an AD prevention trial (the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s study [A4]; n = 2919 cognitively unimpaired; age 71.4±4.8, female 60%). We quantified post‐mortem Aβ and tau in ROSMAP, and used PET to quantify Aβ (florbetapir cortical composite standardized uptake value ratio [SUVR]) and tau (flortaucipir temporal composite SUVR; subset n = 302) in A4. In a subset of ROSMAP participants (n = 31), we evaluated the proportion of activated microglia (PAM) in post‐mortem neocortex. We combined AD GWAS summary statistics with cellular gene expression profiles from human brain single nucleus sequencing, to derive cell‐type‐specific ADPRS (excluding APOE region) in ROSMAP and A4. We assessed the association of each (standardized) cell‐type‐specific ADPRS with AD pathology (linear regression; adjusting for APOE genotypes, age, sex, and first 10 genotype principal components), and examined the correlation between the microglial ADPRS and PAM (Pearson’s correlation). We used Bonferroni‐corrected p‐value threshold, p<6.25×10−3.ResultMicroglial, astrocytic, and endothelial ADPRS were associated with higher Aβ and tau in ROSMAP (Figure 1A‐B). In A4, microglial ADPRS showed the strongest association with Aβ (beta = 0.017, p = 1.6×10−7; Figure 1C), while the association of all other cell‐type‐specific ADPRS with Aβ were no longer significant once adjusted for microglial ADPRS (all p>0.05). Microglial ADPRS showed a suggestive association with temporal composite tau (beta = 0.033, p = 0.012; Figure 1D), even after additionally adjusting for Aβ (beta = 0.024, p = 0.048). Microglial ADPRS was positively correlated with PAM in ROSMAP (Figure 2).ConclusionGenetic risk of AD localizing to microglial genes may contribute to Aβ and tau accumulation and microglial activation, likely starting from the preclinical (cognitively unimpaired) stage. These findings directly connect aggregate microglial AD genetic risk with Aβ and tau accumulation, and provide a genetic rationale to test microglia‐specific interventions in AD prevention trials.

Alzheimer's disease (AD) heritability is enriched in glial genes, but how and when cell-type-specific genetic risk contributes to AD remains unclear. Here, we derive cell-type-specific AD polygenic risk scores (ADPRS) from two extensively characterized datasets. In an autopsy dataset spanning all stages of AD (n=1,457), astrocytic (Ast) ADPRS was associated with both diffuse and neuritic Aβ plaques, while microglial (Mic) ADPRS was associated with neuritic Aβ plaques, microglial activation, tau, and cognitive decline. Causal modeling analyses further clarified these relationships. In an independent neuroimaging dataset of cognitively unimpaired elderly (n=2,921), Ast-ADPRS were associated with Aβ, and Mic-ADPRS was associated with Aβ and tau, showing a consistent pattern with the autopsy dataset. Oligodendrocytic and excitatory neuronal ADPRSs were associated with tau, but only in the autopsy dataset including symptomatic AD cases. Together, our study provides human genetic evidence implicating multiple glial cell types in AD pathophysiology, starting from the preclinical stage.

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