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"The post below describes that "the pitch" has become ever more degrading as more people are put in the position of aggressively hawking more and more elements of their lives on sale…their talents, their time, their apartments, their under-used upscale goods, and even their bodies (and not even in the context of conventional prostitution)."

Trophic rewilding seeks to rehabilitate degraded ecosystems by repopulating them with large animals, thereby re-establishing strong top-down interactions. Yet there are very few tests of whether such initiatives can restore ecosystem structure and functions, and on what timescales. Here we show that war-induced collapse of large-mammal populations in Mozambique's Gorongosa National Park exacerbated woody encroachment by the invasive shrub Mimosa pigra-considered one of the world's 100 worst invasive species-and that one decade of concerted trophic rewilding restored this invasion to pre-war baseline levels.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.