High-dimensional non-Gaussian data analysis based on sample relationship

High-dimensional data are omnipresent. Although many statistical methods developed for analysing high-dimensional data adopt the normality assumption, the Gaussian distribution could be a poor approximation of real data in many applications. In this thesis, we investigate how to properly analyse such high-dimensional non-Gaussian data. As quantifying sample relationships, such as measuring the inter-sample proximity and determining neighbours for samples, is an important step in numerous statistical approaches, this thesis develops three methods for analysing different high-dimensional non-Gaussian data types based on the sample relationship: dimension reduction for single cell RNA-sequencing data with missingness with a proposed proximity measure, dimension reduction for data of small counts with a developed proximity measure, and modelling skewed survival data with a proposed procedure of identifying neighbours for samples. In chapter 3, I develop an unbiased estimator of the Gram matrix, which characterises the proximity between samples. The proposed estimator improves a broad spectrum of dimension reduction methods when applied to single cell RNA-sequencing data with missingness. In addition, the consequences of directly applying existing dimension reduction methods to data with missingness are empirically and theoretically clarified. In chapter 4, I develop a dissimilarity measure for count data with an excess of zeros based on the Kullback-Leibler divergence and the empirical Bayes estimators. The proposed measure is shown to have better discriminative power compared with other popular measures. The proposed measure boosts the performance of standard dimension reduction methods on count data containing many zeros. In chapter 5, I clarify that graphs derived from features themselves can be beneficial for the analysis of high-dimensional survival data when used in graph convolutional networks. Besides, a sequential forward floating selection algorithm is proposed to simultaneously perform survival analysis and unveil the local neighbourhoods of samples with the aid of graph convolutional networks

Dimension Reduction for High-dimensional Small Counts with KL Divergence

Dimension reduction for high-dimensional count data with a large proportion of zeros is an important task in various applications. As a large number of dimension reduction methods rely on the proximity measure, we develop a dissimilarity measure that is well-suited for small counts based on the Kullback-Leibler divergence. We compare the proposed measure with other widely used dissimilarity measures and show that the proposed one has superior discrimination ability when applied to high-dimensional count data having an excess of zeros. Extensive empirical results, on both simulated and publicly-available real-world datasets that contain many zeros, demonstrate that the proposed dissimilarity measure can improve a wide range of dimension reduction methods

Survival Analysis of High-Dimensional Data With Graph Convolutional Networks and Geometric Graphs

This article proposes a survival model based on graph convolutional networks (GCNs) with geometric graphs directly constructed from high-dimensional features. First, we clarify that the graphs used in GCNs play an important role in processing the relational information of samples, and the graphs that align well with the underlying data structure could be beneficial for survival analysis. Second, we show that sparse geometric graphs derived from high-dimensional data are more favorable compared with dense graphs when used in GCNs for survival analysis. Third, from this insight, we propose a model for survival analysis based on GCNs. By using multiple sparse geometric graphs and a proposed sequential forward floating selection algorithm, the new model is able to simultaneously perform survival analysis and unveil the local neighborhoods of samples. The experimental results on real-world datasets show that the proposed survival analysis approach based on GCNs outperforms a variety of existing methods and indicate that geometric graphs can aid survival analysis of high-dimensional data

Loss to Follow-Up from HIV Screening to ART Initiation in Rural China.

BackgroundPatients who are newly screened HIV positive by EIA are lost to follow-up due to complicated HIV testing procedures. Because this is the first step in care, it affects the entire continuum of care. This is a particular concern in rural China.Objective(s)To assess the routine HIV testing completeness and treatment initiation rates at 18 county-level general hospitals in rural Guangxi.MethodsWe reviewed original hospital HIV screening records. Investigators also engaged with hospital leaders and key personnel involved in HIV prevention activities to characterize in detail the routine care practices in place at each county.Results699 newly screened HIV-positive patients between January 1 and June 30, 2013 across the 18 hospitals were included in the study. The proportion of confirmatory testing across the 18 hospitals ranged from 14% to 87% (mean of 43%), and the proportion of newly diagnosed individuals successfully initiated antiretroviral treatment across the hospitals ranged from 3% to 67% (mean of 23%). The average interval within hospitals for individuals to receive the Western Blot (WB) and CD4 test results from HIV positive screening (i.e. achieving testing completion) ranged from 14-116 days (mean of 41.7 days) across the hospitals. The shortest interval from receiving a positive EIA screening test result to receiving WB and CD4 testing and counseling was 0 day and the longest was 260 days.ConclusionThe proportion of patients newly screened HIV positive that completed the necessary testing procedures for HIV confirmation and received ART was very low. Interventions are urgently needed to remove barriers so that HIV patients can have timely access to HIV/AIDS treatment and care in rural China

Assessing the Relationship Between Motor Anticipation and Cortical Excitability in Subacute Stroke Patients With Movement-Related Potentials

Background: Stroke survivors may lack the cognitive ability to anticipate the required control for palmar grasp execution. The cortical mechanisms involved in motor anticipation of palmar grasp movement and its association with post-stroke hand function remains unknown.Aims: To investigate the cognitive anticipation process during a palmar grasp task in subacute stroke survivors and to compare with healthy individuals. The association between cortical excitability and hand function was also explored.Methods: Twenty-five participants with hemiparesis within 1–6 months after first unilateral stroke were recruited. Twenty-five matched healthy individuals were recruited as control. Contingent negative variation (CNV) was measured using electroencephalography recordings (EEG). Event related potentials were elicited by cue triggered hand movement paradigm. CNV onset time and amplitude between pre-cue and before movement execution were recorded.Results: The differences in CNV onset time and peak amplitude were statistically significant between the subacute stroke and control groups, with patients showing earlier onset time with increased amplitudes. However, there was no statistically significant difference in CNV onset time and peak amplitude between lesioned and non-lesioned hemisphere in the subacute stroke group. Low to moderate linear associations were observed between cortical excitability and hand function.Conclusions: The earlier CNV onset time and higher peak amplitude observed in the subacute stroke group suggest increased brain computational demand during palmar grasp task. The lack of difference in CNV amplitude between the lesioned and non-lesioned hemisphere within the subacute stroke group may suggest that the non-lesioned hemisphere plays a role in the motor anticipatory process. The moderate correlations suggested that hand function may be associated with cortical processing of motor anticipation

Assessment of the Quality of Reporting in Abstracts of Randomized Controlled Trials Published in Five Leading Chinese Medical Journals

BACKGROUND: Clear, transparent and sufficiently detailed abstracts of randomized trials (RCTs), published in journal articles are important because readers will often base their initial assessment of a trial on such information. However, little is known about the quality of reporting in abstracts of RCTs published in medical journals in China. METHODS: We identified RCTs abstracts from 5 five leading Chinese medical journals published between 1998 and 2007 and indexed in MEDLINE. We assessed the quality of reporting of these abstracts based on the Consolidated Standards of Reporting Trials (CONSORT) abstract checklist. We also sought to identify whether any differences exist in reporting between the Chinese and English language version of the same abstract. RESULTS: We identified 332 RCT abstracts eligible for examination. Overall, the abstracts we examined reported 0-8 items as designated in the CONSORT checklist. On average, three items were reported per abstract. Details of the interventions (288/332; 87%), the number of participants randomized (216/332; 65%) and study objectives (109/332; 33%) were the top three items reported. Only two RCT abstracts reported details of trial registration, no abstracts reported the method of allocation concealment and only one mentioned specifically who was blinded. In terms of the proportion of RCT abstracts fulfilling a criterion, the absolute difference (percentage points) between the Chinese and English abstracts was 10% (ranging from 0 to 25%) on average, per item. CONCLUSIONS: The quality of reporting in abstracts of RCTs published in Chinese medical journals needs to be improved. We hope that the introduction and endorsement of the CONSORT for Abstracts guidelines by journals reporting RCTs will lead to improvements in the quality of reporting

Biogenic factors explain soil carbon in paired urban and natural ecosystems worldwide

12 páginas.- 4 figuras.- 49 referencia.- Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41558-023-01646-z .- Full-text access to a view-only version (Acceso a texto completo de sólo lectura en este enlace) https://rdcu.be/c8vZiUrban greenspaces support multiple nature-based services, many of which depend on the amount of soil carbon (C). Yet, the environmental drivers of soil C and its sensitivity to warming are still poorly understood globally. Here we use soil samples from 56 paired urban greenspaces and natural ecosystems worldwide and combine soil C concentration and size fractionation measures with metagenomics and warming incubations. We show that surface soils in urban and natural ecosystems sustain similar C concentrations that follow comparable negative relationships with temperature. Plant productivity’s contribution to explaining soil C was higher in natural ecosystems, while in urban ecosystems, the soil microbial biomass had the greatest explanatory power. Moreover, the soil microbiome supported a faster C mineralization rate with experimental warming in urban greenspaces compared with natural ecosystems. Consequently, urban management strategies should consider the soil microbiome to maintain soil C and related ecosystem services.This study was supported by a 2019 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation (URBANFUN), and by BES Grant Agreement No. LRB17\1019 (MUSGONET). M.D-B., P.G-P., J.D. and A.R. acknowledge support from TED2021-130908B-C41/AEI/10.13039/501100011033/ Unión Europea NextGenerationEU/PRTR. M.D.-B. also acknowledges support from the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. M.D.-B. was also supported by a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático ‘01 - Refuerzo de la investigación, el desarrollo tecnológico y la innovación’) associated with the research project P20_00879 (ANDABIOMA). D.J.E. was supported by the Hermon Slade Foundation. J.P.V. thanks the Science and Engineering Research Board (SERB) (EEQ/2021/001083, SIR/2022/000626) and the Department of Science and Technology (DST), India (DST/INT/SL/P-31/2021) and Banaras Hindu Univeristy-IoE (6031)-incentive grant for financial assistance for research in plant-microbe interaction and soil microbiome. J.D. and A. Rodríguez acknowledge support from the FCT (2020.03670.CEECIND and SFRH/BDP/108913/2015, respectively), as well as from the MCTES, FSE, UE and the CFE (UIDB/04004/2021) research unit financed by FCT/MCTES through national funds (PIDDAC).Peer reviewe

Global hotspots for soil nature conservation

19 páginas.- 5 figuras.- 98 referencias.- Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41586-022-05292-xSoils are the foundation of all terrestrial ecosystems1. However, unlike for plants and animals, a global assessment of hotspots for soil nature conservation is still lacking2. This hampers our ability to establish nature conservation priorities for the multiple dimensions that support the soil system: from soil biodiversity to ecosystem services. Here, to identify global hotspots for soil nature conservation, we performed a global field survey that includes observations of biodiversity (archaea, bacteria, fungi, protists and invertebrates) and functions (critical for six ecosystem services) in 615 composite samples of topsoil from a standardized survey in all continents. We found that each of the different ecological dimensions of soils—that is, species richness (alpha diversity, measured as amplicon sequence variants), community dissimilarity and ecosystem services—peaked in contrasting regions of the planet, and were associated with different environmental factors. Temperate ecosystems showed the highest species richness, whereas community dissimilarity peaked in the tropics, and colder high-latitudinal ecosystems were identified as hotspots of ecosystem services. These findings highlight the complexities that are involved in simultaneously protecting multiple ecological dimensions of soil. We further show that most of these hotspots are not adequately covered by protected areas (more than 70%), and are vulnerable in the context of several scenarios of global change. Our global estimation of priorities for soil nature conservation highlights the importance of accounting for the multidimensionality of soil biodiversity and ecosystem services to conserve soils for future generations.This project received funding from the British Ecological Society (agreement LRA17\1193; MUSGONET). C.A.G. and N.E. were funded by DFG–FZT 118, 202548816; C.A.G. was supported by FCT-PTDC/BIA-CBI/2340/2020; M.D.-B. was supported by RYC2018-025483-I, PID2020-115813RA-I00\MCIN/AEI/10.13039/501100011033 and P20_00879. M.A.M.-M. and S.A. were funded by FONDECYT 1181034 and ANID-PIA-Anillo INACH ACT192057. J.D. and A.R. acknowledge support from IF/00950/2014, 2020.03670.CEECIND, SFRH/BDP/108913/2015 and UIDB/04004/2020. Y.-R.L. was supported by 2662019PY010 from the FRFCU. L.T. was supported by the ESF grant PRG632. F.B. and J.L.M. were supported by i-LINK+2018 (LINKA20069) funded by CSIC. C.T.-D. was supported by the Grupo de Biodibersidad & Cambio Global UBB–GI 170509/EF. C.P. was supported by the EU H2020 grant agreement 101000224. H.C. was supported by NSFC32101335, FRFCU2412021QD014 and CPSF2021M690589. J.P.V. was supported by DST (DST/INT/SL/P-31/2021) SERB (EEQ/2021/001083) and BHU-IoE (6031).Peer reviewe

Continuous Long Time Series Monitoring of Urban Construction Land in Supporting the SDG 11.3.1—A Case Study of Nanning, Guangxi, China

Enhancing the spatio-temporal dynamic monitoring of the construction land of emerging major cities provides the basis for understanding the effects of human activities on the earth’s ecological environment changes, which is regarded as an important way to improve human wellbeing and to achieve the UN SDGs. This paper, which uses Nanning as an example, adopts 30 m annual remote sensing land cover datasets from 1990–2021 to elaborately analyze the temporal evolution and spatial expansion of construction land and to evaluate the sustainable development level based on SDG 11.3.1. The conclusions of this paper are as follows: (1) For Nanning City, during 1990–2021, construction land area increased from 54.77 km2 to 326.33 km2—a nearly five-fold increase. Spreading expansion and finger-like expansion along roads played a leading role during the development of the construction land in Nanning. Liangqing and Yongning are the future directions of urban development. The LCRPGR of Nanning declined with one peak period around 1995, and the LCRPGR was relatively stable with a high intensity of land use. (2) For the other emerging major cities, continuous long duration data are more suitable for urban construction land monitoring compared with traditional sparse time-series monitoring. The rich information derived from continuous long duration data can help decision makers to formulate and implement more comprehensive measures. The research paradigm adopted in this study can be applied in other cities. The newly developed urban districts will have foreseeable high sustainable development risks, and urban development strategies at the international, national, and urban levels could reduce those risks and promote the realization of UN SDGs from different angles and with different intensities