Adaptive evolution of the enigmatic Takakia now facing climate change in Tibet.

The most extreme environments are the most vulnerable to transformation under a rapidly changing climate. These ecosystems harbor some of the most specialized species, which will likely suffer the highest extinction rates. We document the steepest temperature increase (2010-2021) on record at altitudes of above 4,000 m, triggering a decline of the relictual and highly adapted moss Takakia lepidozioides. Its de-novo-sequenced genome with 27,467 protein-coding genes includes distinct adaptations to abiotic stresses and comprises the largest number of fast-evolving genes under positive selection. The uplift of the study site in the last 65 million years has resulted in life-threatening UV-B radiation and drastically reduced temperatures, and we detected several of the molecular adaptations of Takakia to these environmental changes. Surprisingly, specific morphological features likely occurred earlier than 165 mya in much warmer environments. Following nearly 400 million years of evolution and resilience, this species is now facing extinction.

Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut.

In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation.

A Light Field Full-Focus Image Feature Point Matching Method with an Improved ORB Algorithm.

Most of the traditional image feature point extraction and matching methods are based on a series of light properties of images. These light properties easily conflict with the distinguishability of the image features. The traditional light imaging methods focus only on a fixed depth of the target scene, and subjects at other depths are often easily blurred. This makes the traditional image feature point extraction and matching methods suffer from a low accuracy and a poor robustness. Therefore, in this paper, a light field camera is used as a sensor to acquire image data and to generate a full-focus image with the help of the rich depth information inherent in the original image of the light field. The traditional ORB feature point extraction and matching algorithm is enhanced with the goal of improving the number and accuracy of the feature point extraction for the light field full-focus images. The results show that the improved ORB algorithm extracts not only most of the features in the target scene but also covers the edge part of the image to a greater extent and produces extracted feature points which are evenly distributed for the light field full-focus image. Moreover, the extracted feature points are not repeated in a large number in a certain part of the image, eliminating the aggregation phenomenon that exists in traditional ORB algorithms.

An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement.

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19.

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

Spatial and temporal variation characteristics of atmospheric NO2 and SO2 in the Beijing-Tianjin-Hebei region before and after the COVID-19 outbreak.

Emergency response mechanisms were activated throughout China during the COVID-19 outbreak. It is different from the temporary, partial, and limited pollution control measures taken to ensure the regional environmental quality during several important events such as the 2008 Beijing Olympic Games and the 2014 Asia-Pacific Economic Cooperation (APEC). During the COVID-19 epidemic period, extensive movement of people and almost all unnecessary industrial production (necessary industrial production refers to the production of food, epidemic prevention materials, etc.) have been severely restricted, so transportation and industrial production have been greatly reduced. This is a rare extreme emission reduction scenario that presents a unique opportunity for atmospheric research. In this study, based on hourly mass concentration data of NO2 and SO2 from atmospheric monitoring sites in the Beijing-Tianjin-Hebei (BTH) region during the COVID-19 epidemic period, the changes in transportation and industrial production in the region, data statistics, and spatial analysis were used to analyze the pollution changes and their causes. The results indicate that the NO2 and SO2 concentrations in the BTH region decreased significantly during the epidemic period. The spatial distribution pattern of NO2 pollution in the BTH region was "high in the southeast and low in the northwest," and SO2 pollution in the BTH region was high in the southern and eastern parts of Hebei. The initiation of emergency response level 1 had an obvious effect on reducing NO2 and SO2 pollution in the region, while the impact of emergency response level 2 and below was limited. Compared with the single traffic control, the comprehensive control, similar to the emergency response, had a better effect on reducing NO2 pollution in the region. The control of major large cities in the region also had a certain effect on alleviating NO2 and SO2 pollution in the entire region. Moreover, for activities under short-term control, it is particularly important to guard against the "retaliatory growth" after the control is lifted. By reducing and controlling some polluting industries in industrial production, the degree of NO2 and SO2 pollution in the region can be effectively reduced. The manufacturing industry of chemical raw materials and the chemical products and non-metallic mineral products industry made a great contribution to the change in industrial source pollution emissions in the BTH region during the COVID-19 epidemic. Road traffic emissions remained an important source of NO2 emissions in the BTH region during this period. NO2 emission reduction can be effectively achieved by controlling road traffic and transportation.