Penaeid shrimp genome provides insights into benthic adaptation and frequent molting

Crustacea, the subphylum of Arthropoda which dominates the aquatic environment, is of major importance in ecology and fisheries. Here we report the genome sequence of the Pacific white shrimp Litopenaeus vannamei, covering similar to 1.66 Gb (scaffold N50 605.56 Kb) with 25,596 protein-coding genes and a high proportion of simple sequence repeats (>23.93%). The expansion of genes related to vision and locomotion is probably central to its benthic adaptation. Frequent molting of the shrimp may be explained by an intensified ecdysone signal pathway through gene expansion and positive selection. As an important aquaculture organism, L. vannamei has been subjected to high selection pressure during the past 30 years of breeding, and this has had a considerable impact on its genome. Decoding the L. vannamei genome not only provides an insight into the genetic underpinnings of specific biological processes, but also provides valuable information for enhancing crustacean aquaculture

Condensed Representation Learning for Interactive Driving Styles Recognition

Automated vehicle (AV) validation faces the "billions of miles" challenge, requiring high-fidelity simulations to replicate diverse interactive driving behaviors for safety. Traditional methods oversimplify by using uniform behavioral models, ignoring the diversity of human driving styles, which are deeply influenced by individual psychological traits. This research introduces a condensed framework for representing interactive driving styles, by incorporating these psychological dimensions, balancing completeness and complexity. Key features include: i) individual style recognition via attention mechanisms and hierarchical contrastive learning, capturing subtle cognitive-based interaction patterns that reflect underlying differences in driver psychology (e.g., risk tolerance, decision-making heuristics); ii) scenario-independent style compression, filtering external factors to extract intrinsic driver intentions; iii) dimensionality-aware refinement, mapping complex behaviors to low-dimensional psychological axes for efficient computation. Tests on the NGSIM dataset reduced testing complexity by decoupling styles from scenarios. Compared to traditional methods, style distinctiveness improves by 28% (entropy-based), with 85% edge-case behavior coverage. This framework supports scalable AV testing by integrating diverse, psychologically-informed driving styles without combinatorial complexity

A BAC-Based Physical Map of Zhikong Scallop (Chlamys farreri Jones et Preston)

Zhikong scallop (Chlamys farreri) is one of the most economically important aquaculture species in China. Physical maps are crucial tools for genome sequencing, gene mapping and cloning, genetic improvement and selective breeding. In this study, we have developed a genome-wide, BAC-based physical map for the species. A total of 81,408 clones from two BAC libraries of the scallop were fingerprinted using an ABI 3130xl Genetic Analyzer and a fingerprinting kit developed in our laboratory. After data processing, 63,641 (∼5.8× genome coverage) fingerprints were validated and used in the physical map assembly. A total of 3,696 contigs were assembled for the physical map. Each contig contained an average of 10.0 clones, with an average physical size of 490 kb. The combined total physical size of all contigs was 1.81 Gb, equivalent to approximately 1.5 fold of the scallop haploid genome. A total of 10,587 BAC end sequences (BESs) and 167 markers were integrated into the physical map. We evaluated the physical map by overgo hybridization, BAC-FISH (fluorescence in situ hybridization), contig BAC pool screening and source BAC library screening. The results have provided evidence of the high reliability of the contig physical map. This is the first physical map in mollusc; therefore, it provides an important platform for advanced research of genomics and genetics, and mapping of genes and QTL of economical importance, thus facilitating the genetic improvement and selective breeding of the scallop and other marine molluscs

Anything in Any Scene: Photorealistic Video Object Insertion

Realistic video simulation has shown significant potential across diverse applications, from virtual reality to film production. This is particularly true for scenarios where capturing videos in real-world settings is either impractical or expensive. Existing approaches in video simulation often fail to accurately model the lighting environment, represent the object geometry, or achieve high levels of photorealism. In this paper, we propose Anything in Any Scene, a novel and generic framework for realistic video simulation that seamlessly inserts any object into an existing dynamic video with a strong emphasis on physical realism. Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism. We experimentally demonstrate that Anything in Any Scene framework produces simulated videos of great geometric realism, lighting realism, and photorealism. By significantly mitigating the challenges associated with video data generation, our framework offers an efficient and cost-effective solution for acquiring high-quality videos. Furthermore, its applications extend well beyond video data augmentation, showing promising potential in virtual reality, video editing, and various other video-centric applications. Please check our project website https://anythinginanyscene.github.io for access to our project code and more high-resolution video results

Deepened winter snow cover enhances net ecosystem exchange and stabilizes plant community composition and productivity in a temperate grassland

Global warming has greatly altered winter snowfall patterns, and there is a trend towards increasing winter snow in semi-arid regions in China. Winter snowfall is an important source of water during early spring in these water-limited ecosystems, and it can also affect nutrient supply. However, we know little about how changes in winter snowfall will affect ecosystem productivity and plant community structure during the growing season. Here, we conducted a 5-year winter snow manipulation experiment in a temperate grassland in Inner Mongolia. We measured ecosystem carbon flux from 2014 to 2018 and plant biomass and species composition from 2015 to 2018. We found that soil moisture increased under deepened winter snow in early growing season, particularly in deeper soil layers. Deepened snow increased the net ecosystem exchange of CO 2 (NEE) and reduced intra- and inter-annual variation in NEE. Deepened snow did not affect aboveground plant biomass (AGB) but significantly increased root biomass. This suggested that the enhanced NEE was allocated to the belowground, which improved water acquisition and thus contributed to greater stability in NEE in deep-snow plots. Interestingly, the AGB of grasses in the control plots declined over time, resulting in a shift towards a forb-dominated system. Similar declines in grass AGB were also observed at three other locations in the region over the same time frame and are attributed to 4 years of below-average precipitation during the growing season. By contrast, grass AGB was stabilized under deepened winter snow and plant community composition remained unchanged. Hence, our study demonstrates that increased winter snowfall may stabilize arid grassland systems by reducing resource competition, promoting coexistence between plant functional groups, which ultimately mitigates the impacts of chronic drought during the growing season

Synthesis of ultrahigh-metal-density single-atom catalysts via metal sulfide-mediated atomic trapping

Single-atom catalysts (SACs) exhibit exceptional intrinsic activity per metal site, but are often limited by low metal loading, which compromises the overall catalytic performance. Pyrolytic strategies commonly used for synthesizing SACs generally suffer from aggregation at high metal loadings. Here we report a universal synthesis approach for ultrahigh-density metal–nitrogen–carbon (UHDM–N–C) SACs via a metal-sulfide-mediated atomization process. We show that our approach is general for transition, rare-earth and noble metals, achieving 17 SACs with metal loadings >20 wt% (including a loading of 26.9 wt% for Cu, 31.2 wt% for Dy and 33.4 wt% for Pt) at 800 °C, as well as high-entropy quinary and vicenary SACs with ultrahigh metal contents. In situ X-ray diffraction and transmission electron microscopy alongside molecular simulations reveals a dynamic nanoparticle-to-single atom transformation process, including thermally driven decomposition of the metal sulfide and the trapping of liberated metal atoms to form thermodynamically stable M–N–C moieties. Our studies indicate that a high N-doping is crucial for achieving ultrahigh-loading metal atoms and a metal-sulfide-mediated process is essential for avoiding metal aggregation at high loadings. As a demonstration, the metal-loading-dependent activity in electrocatalytic oxygen evolution reaction is demonstrated on SACs with increasing Ni content. (Figure presented.

Ecosystem Carbon Stock Influenced by Plantation Practice: Implications for Planting Forests as a Measure of Climate Change Mitigation

Uncertainties remain in the potential of forest plantations to sequestrate carbon (C). We synthesized 86 experimental studies with paired-site design, using a meta-analysis approach, to quantify the differences in ecosystem C pools between plantations and their corresponding adjacent primary and secondary forests (natural forests). Totaled ecosystem C stock in plant and soil pools was 284 Mg C ha−1 in natural forests and decreased by 28% in plantations. In comparison with natural forests, plantations decreased aboveground net primary production, litterfall, and rate of soil respiration by 11, 34, and 32%, respectively. Fine root biomass, soil C concentration, and soil microbial C concentration decreased respectively by 66, 32, and 29% in plantations relative to natural forests. Soil available N, P and K concentrations were lower by 22, 20 and 26%, respectively, in plantations than in natural forests. The general pattern of decreased ecosystem C pools did not change between two different groups in relation to various factors: stand age (<25 years vs. ≥25 years), stand types (broadleaved vs. coniferous and deciduous vs. evergreen), tree species origin (native vs. exotic) of plantations, land-use history (afforestation vs. reforestation) and site preparation for plantations (unburnt vs. burnt), and study regions (tropic vs. temperate). The pattern also held true across geographic regions. Our findings argued against the replacement of natural forests by the plantations as a measure of climate change mitigation