simonvh/fluff: Version 2.1.4

Release notes: Added scale bar to heatmap Fixed issue with tabix-index files

Spatial Effects of Technological Progress and Financial Support on China’s Provincial Carbon Emissions

The spatial autocorrelation analysis method was applied to panel data from the provinces of China (including autonomous regions and municipalities directly under the central government) for the period 2003 to 2016 in order to construct a spatial Durbin model of technological progress and financial support in relation to reductions in carbon emissions. The results show that China’s carbon intensity presents significant spatial spillover effects under different spatial weights, which indicates that the carbon intensity of a province is influenced not only by its own characteristics, but also by the carbon emission behaviors of geographically adjacent and economically similar provinces and regions. Financial structure, financial scale, and financial efficiency all have significant effects on carbon intensity within a province, while financial structure is also linked to carbon intensity in other regions, but financial scale has no significant spillover effect on carbon intensity in space. Areas with high financial efficiency can reduce their own carbon intensity as well as that of surrounding areas. The inter-regional spillover effect of technological progress on carbon intensity is stronger than the spillover effect, but there is a time lag

Understanding land use, livelihood and health transition of Tibetan nomads : case from Gangga Township, Dingri County, Tibetan Autonomous Region of China

Due to copyright restrictions, this item cannot be sharedThe Tibetan Nomads in the Tibetan Autonomous Region of China have undergone profound transitions in recent decades with important implications for conservation, livelihood and human health. The changes from traditional nomads to agro-pastoralists to permanent agriculture, a sedentary village life (known as ‘sendentarisation’) has been associated with a remarkable change in the dietary diversity and lifestyle with decline in space mobility, increase in food production and emergence of both infectious and chronic diseases. The overarching response of the government has been to emphasize on the infrastructure and technological solutions. The local adaptation strategy of Tibetan nomads through maintaining the balanced mobile herding, reindeer husbandry as well as off-farm labor and trade would cause both the environmental degradation as well as improving the well-being of the local people. Drawing on transdisciplinary approach a preliminary fieldwork in Gangga Township of Dingri County at the foothills of Mt. Everest, was conducted. A pertinent linkage between land use and human health, and spatial and temporal mismatch of livelihoods and healthcare services was identified in transition to sedentary village life. We suggest the emerging imperatives by using ecosystem approach to human health to help improve Tibetan livelihood in transition from nomads to sedentary life

Risk Prediction and Variable Analysis of Pine Wilt Disease by a Maximum Entropy Model

Pine wilt disease (PWD) has caused a huge damage to pine forests. PWD is mainly transmitted by jumping diffusion, affected by insect vectors and human activities. Since the results of climate change, pine wood nematode (PWN—Bursaphelenchus xylophilus) has begun invading the temperate zones and higher elevation area. In this situation, predicting the distribution of PWD is an important part of the prevention and control of the epidemic situation. The research established the Maxent model to conduct a multi-angle, fine-scale prediction on the risk distribution of PWD. We adjusted two parameters, regularization multiplier (RM) and feature combination (FC), to optimize the model. Influence factors were selected and divided into natural, landscape, and human variables, according to the physical characteristics and spread rules of PWD. The middle-suitability regions and high-suitability regions are distributed in a Y-shape, and divided the study area into three parts. The high-suitability areas are concentrated in the region with high temperature, low elevation, and intensive precipitation. Among the selected variables, natural factors still play the most important role in the distribution of the disease, and human factors and landscape factors are also worked well. The permutation importance of factors is different due to differences in climate and other conditions in different regions. The multi-angle, fine-scale model can help provide useful information for effective control and tactical management of PWD

zqfang/GSEApy: gseapy-1.1.0

<h2>What's Changed</h2> <p>New:</p> <ul> <li>Add <code>gsva</code> module: This is the Rust implementation of <code>GSVA</code> algorithm. #226</li> </ul> <p>Fixed:</p> <ul> <li>#211, Now, <code>pheno_pos</code> and <code>pheno_neg</code> can define with</li> </ul> <pre><code class="language-python">from gseapy import GSEA gs = GSEA(data="./tests/extdata/Leukemia_hgu95av2.trim.txt", gene_sets='KEGG_2016', classes = "./tests/extdata/Leukemia.cls" ) # set here gs.pheno_pos = ... gs.pheno_neg = ... gs.run() </code></pre> <ul> <li>improvement for <code>barplot</code> #224. Specify colors for each group explicity</li> </ul> <pre><code class="language-python">barplot( ...., color= {'KEGG_2021_Human': 'salmon', 'MSigDB_Hallmark_2020':'darkblue'}) </code></pre> <p><strong>Full Changelog</strong>: https://github.com/zqfang/GSEApy/compare/v1.0.6...v1.1.0</p&gt

zqfang/GSEApy: gseapy-v1.1.1

<h2>What's Changed</h2> <ul> <li><p>Corrected odds ratio formula, #237 by @136s in https://github.com/zqfang/GSEApy/pull/238</p> </li> <li><p>Refactor internal data parser for files .rnk, .gct. etc</p> </li> </ul> <p><strong>Full Changelog</strong>: https://github.com/zqfang/GSEApy/compare/v1.1.0...v1.1.1</p&gt