Northern China harbored the world's earliest complex societies based on millet farming, in two major centers in the Yellow (YR) and West Liao (WLR) River basins. Until now, their genetic histories have remained largely unknown. Here we present 55 ancient genomes dating to 7500-1700 BP from the YR, WLR, and Amur River (AR) regions. Contrary to the genetic stability in the AR, the YR and WLR genetic profiles substantially changed over time. The YR populations show a monotonic increase over time in their genetic affinity with present-day southern Chinese and Southeast Asians. In the WLR, intensification of farming in the Late Neolithic is correlated with increased YR affinity while the inclusion of a pastoral economy in the Bronze Age was correlated with increased AR affinity. Our results suggest a link between changes in subsistence strategy and human migration, and fuel the debate about archaeolinguistic signatures of past human migration.

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The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

A logistic regression model for Semantic Web service matchmaking SCIENCE CHINA Information Sciences 55, 1715 (2012); An accelerator for the logistic regression algorithm based on sampling on-demand SCIENCE CHINA Information Sciences 63, 169102 (2020); AGE-DEPENDENT LOGISTIC REGRESSION MODEL AND ITS APPLICATION Science in China Series B-Chemistry, Life Sciences & Earth Sciences 34, 446 (1991); An investigation of influential factors of downgrade truck crashes: A logistic regression approach

Evidence concerning the association between ambient gaseous air pollutant exposures and semen quality is sparse, and findings in previous studies remain largely inconsistent. We enrolled 1759 men with 2184 semen examinations at a large reproductive medical center in Wuhan, China between 2013 and 2015. Inverse distance weighting interpolation was performed to estimate individual exposures to SO2, NO2, CO and O3 during the entire period (lag 0–90 days) and key periods (lag 0-9, 10-14, 70-90 days) of sperm development. Linear mixed models were used to analyze exposure-response relationships. SO2 exposure with 0-90 days lag was significantly associated with monotonically decreased sperm concentration (β for each interquartile range increase of exposure: −0.14; 95% CI: −0.23, −0.05), sperm count (−0.21; −0.30, −0.12) and total motile sperm count (−0.16; −0.25, −0.08). Significant associations were observed for total and progressive motility only when SO2 exposure was at the highest quintile (all Ptrend < 0.05). Similar trends were observed for SO2 exposure with 70-90 days lag. NO2, CO, or O3 exposure was not significantly associated with semen quality. Our results suggest that ambient SO2 exposure adversely affects semen quality, and highlight the potential to improve semen quality by reducing ambient SO2 exposure during early stage of sperm development.

This paper outlines a model for the domestication of Panicum miliaceum (broomcorn millet) in Northern China. Data from 43 archaeological sites indicate a continuous increase in average grain size between 6000 and 3300 bc. After this date there is a divergence, with grain size continuing to increase in some populations, while others show no further size increase. The initial increase in grain size is attributed to selection during domestication, while later divergence after 3300 bc is interpreted as resulting from post-domestication selection. Measurements of grains from two archaeological populations of P. ruderale, showed grains were longer in length by 3300 bc than the earliest grains of P. miliaceum. This suggests this sub-species includes many feral, weedy and/or introgressed forms of P. miliaceum and therefore is probably not entirely representative of the true wild ancestor. It is argued that changes from shattering to non-shattering are contemporary with increasing grain size and the commencement of cultivation. The window of P. miliaceum domestication is therefore likely to lie between 7000 and 3300 bc. However, it is probable that a lengthy period of millet harvesting and small-scale management preceded its domestication.

BackgroundOne of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients’ gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients’ clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified.MethodsTo try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease.ResultsThe results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets.ConclusionsThe advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients’ survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi-supervised learning model is one more appropriate tool for survival analysis in clinical cancer research.