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Abstract Larynx cancer poses a significant public health challenge in China, with rising incidence and mortality rates over the past decades. Understanding the long-term trends and underlying factors is crucial for effective intervention and policy formulation. Data were utilized from the global burden of disease (GBD) Study 2021 to analyze the incidence, prevalence, mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) due to larynx cancer in China from 1990 to 2021. Joinpoint regression analysis identified key changes in trends, while age-period-cohort (APC) analysis and decomposition analysis quantified the contributions of aging, epidemiological changes, and population growth to these trends. Our study found a significant increase in the incidence and prevalence of larynx cancer in China, particularly among males. The age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) for males were substantially higher than those for females. Decomposition analysis revealed that aging was the primary driver of increasing incidence and mortality rates, while epidemiological changes had a mitigating effect. Joinpoint analysis identified periods of rapid urbanization and economic growth as key turning points for increased incidence. Bayesian APC models projected a continued upward trend in incidence rates up to 2030. The rising burden of larynx cancer in China underscores the need for targeted public health interventions, including smoking cessation programs, environmental pollution control, and early detection strategies. Addressing gender disparities and implementing effective prevention measures are crucial to mitigating the impact of larynx cancer in China.

Ground filtering is a fundamental step in airborne LiDAR data processing toward a variety of applications. However, existing algorithms remain tremendously challenging in complex environments, e.g. steep hillsides, ridges, valleys, discontinuities, and numerous objects. We presented a new ground filtering algorithm that can handle various landscapes. First, the multi-view window is developed to increase the number of ground seeds on the various terrains. Second, multi-resolution hierarchical cloth simulation is used to rapidly construct the high-resolution reference terrain, and bidirectional internal force operation is proposed to improve the accuracy of reference terrain by smoothing the spikes in cloth. Finally, ground and non-ground points are classified based on the height differences between points and the reference terrain. The proposed algorithm was validated not only in the International Society for Photogrammetry and Remote Sensing (ISPRS) but also karst datasets, where particularly complex environments is contained. Results showed that the proposed algorithm outperformed the existing algorithms, with the lowest average total error of 3.85% and the highest average kappa coefficient of 87.75%. Moreover, the proposed algorithm can completely preserve complex terrain, e.g. extremely steep hillsides, and sharp ridges. This study had great potential to provide a useful tool for LiDAR data processing.

In the rapidly urbanizing world, as one of the distinct anthropogenic alterations of global climate change, global warming has attracted rising concerns due to its negative effects on human well-being and biodiversity. However, existing studies mostly focused on the difference in temperature elevation among urbanized areas and non-urbanized areas, i.e., rural or suburban areas. The allometric urban warming at intra-urban scales was overlooked. This research aimed to expand our understanding of urbanization–temperature relationships by applying a concept of a “previous-new” dichotomy of urbanized areas. To quantify the land surface temperature (LST) dynamics of 340 cities in China, we analyzed the LST of different land use types through trend analysis and absolute change calculation models. The urban heat island (UHI) effect of two spatial units, i.e., newly expanded urbanized area (“new UA” hereinafter) during 2000–2015 and previously existing urbanized area (“previous UA” hereinafter) in 2000, were compared and discussed. Our findings reveal that urban growth in China coincided with an LST increase of approximately 0.68 °C across the entire administrative boundary, with higher increases observed in regions between the Yellow River and Yangtze River and lower increases in other areas. Moreover, the new UA exhibited significantly greater LST increases and urban heat island intensity (HUII) compared to the previous UA. The dynamics of LST corresponded to the speed and scale of urban growth, with cities experiencing higher growth rates and percentages exhibiting more pronounced LST increases. This study reveals the impact of the underlying surface on human settlements on a large scale.

Abstract Background Accurate prediction of compound-protein interaction (CPI) plays a crucial role in drug discovery. Existing data-driven methods aim to learn from the chemical structures of compounds and proteins yet ignore the conceptual knowledge that is the interrelationships among the fundamental elements in the biomedical knowledge graph (KG). Knowledge graphs provide a comprehensive view of entities and relationships beyond individual compounds and proteins. They encompass a wealth of information like pathways, diseases, and biological processes, offering a richer context for CPI prediction. This contextual information can be used to identify indirect interactions, infer potential relationships, and improve prediction accuracy. In real-world applications, the prevalence of knowledge-missing compounds and proteins is a critical barrier for injecting knowledge into data-driven models. Results Here, we propose BEACON, a data and knowledge dual-driven framework that bridges chemical structure and conceptual knowledge for CPI prediction. The proposed BEACON learns the consistent representations by maximizing the mutual information between chemical structure and conceptual knowledge and predicts the missing representations by minimizing their conditional entropy. BEACON achieves state-of-the-art performance on multiple datasets compared to competing methods, notably with 5.1% and 6.6% performance gain on the BIOSNAP and DrugBank datasets, respectively. Moreover, BEACON is the only approach capable of effectively predicting knowledge representations for knowledge-lacking compounds and proteins. Conclusions Overall, our work provides a general approach for directly injecting conceptual knowledge to enhance the performance of CPI prediction.

The growing microbial resistance to traditional medicines necessitates in-depth analysis of medicine-microbe interactions (MMIs) to develop new therapeutic strategies. Widely used artificial intelligence models are limited by sparse observational data and prevalent noise, leading to over-reliance on specific data for feature extraction and reduced generalization ability. To address these limitations, we integrate Kolmogorov-Arnold Networks (KANs), independent subspaces, and collaborative decoding techniques into the masked graph autoencoder (Mask GAE) framework, creating an innovative MMI prediction model with enhanced accuracy, generalization, and interpretability. First, we apply Bernoulli distribution to randomly mask parts of the medicine-microbe graph, advancing self-supervised training and reducing noise impact. Additionally, the independent subspace technique enables graph neural networks (GNNs) to learn weights independently across different feature subspaces, enhancing feature expression. Fusing the multi-layer outputs of GNNs effectively reduces information loss caused by masking. Moreover, using KANs for advanced nonlinear mapping enhances the learnability and interpretability of weights, deepening the understanding of complex MMIs. These measures significantly enhanced the accuracy, generalization, and interpretability of our model in MMI prediction tasks. We validated our model on three public datasets with results showing that our model outperformed existing leading models. The relevant data and code are publicly accessible at: https://github.com/zhuoninnin1992/MKAN-MMI.

Abstract Background Although multiple chicken genomes have been assembled and annotated, the numbers of protein-coding genes in chicken genomes and their variation among breeds are still uncertain due to the low quality of these genome assemblies and limited resources used in their gene annotations. To fill these gaps, we recently assembled genomes of four indigenous chicken breeds with distinct traits at chromosome-level. In this study, we annotated genes in each of these assembled genomes using a combination of RNA-seq- and homology-based approaches. Results We identified varying numbers (17,497–17,718) of protein-coding genes in the four indigenous chicken genomes, while recovering 51 of the 274 “missing” genes in birds in general, and 36 of the 174 “missing” genes in chickens in particular. Intriguingly, based on deeply sequenced RNA-seq data collected in multiple tissues in the four breeds, we found 571 ~ 627 protein-coding genes in each genome, which were missing in the annotations of the reference chicken genomes (GRCg6a and GRCg7b/w). After removing redundancy, we ended up with a total of 1,420 newly annotated genes (NAGs). The NAGs tend to be found in subtelomeric regions of macro-chromosomes (chr1 to chr5, plus chrZ) and middle chromosomes (chr6 to chr13, plus chrW), as well as in micro-chromosomes (chr14 to chr39) and unplaced contigs, where G/C contents are high. Moreover, the NAGs have elevated quadruplexes G frequencies, while both G/C contents and quadruplexes G frequencies in their surrounding regions are also high. The NAGs showed tissue-specific expression, and we were able to verify 39 (92.9%) of 42 randomly selected ones in various tissues of the four chicken breeds using RT-qPCR experiments. Most of the NAGs were also encoded in the reference chicken genomes, thus, these genomes might harbor more genes than previously thought. Conclusion The NAGs are widely distributed in wild, indigenous and commercial chickens, and they might play critical roles in chicken physiology. Counting these new genes, chicken genomes harbor more genes than originally thought.

Abstract Background Although many studies have been done to reveal artificial selection signatures in commercial and indigenous chickens, a limited number of genes have been linked to specific traits. To identify more trait-related artificial selection signatures and genes, we re-sequenced a total of 85 individuals of five indigenous chicken breeds with distinct traits from Yunnan Province, China. Results We found 30 million non-redundant single nucleotide variants and small indels (

Abstract Many lines of evidence indicate that red jungle fowl (RJF) is the primary ancestor of domestic chickens. Although multiple versions of RJF (galgal2-galgal5 and GRCg6a) and commercial chickens (GRCg7b/w and Huxu) genomes have been assembled since 2004, no high-quality indigenous chicken genomes have been assembled, hampering the understanding of chicken domestication and evolution. To fill the gap, we sequenced the genomes of four indigenous chickens with distinct morphological traits in southwest China, using a combination of short, long and Hi-C reads. We assembled each genome (~1.0 Gb) into 42 chromosomes with chromosome N50 90.5–90.9 Mb, amongst the highest quality of chicken genome assemblies. To provide resources for gene annotation and functional analysis, we also sequenced transcriptomes of 10 tissues for each of the four chickens. Moreover, we corrected many mis-assemblies and assembled missing micro-chromosomes 29 and 34–39 for GRCg6a. Our assemblies, sequencing data and the correction of GRCg6a can be valuable resources for studying chicken domestication and evolution.

Abstract There are four species in the Crossoptilon genus inhibiting at from very low to very high altitudes across China, and they are in varying levels of danger of extinction. To better understand the genetic basis of adaptation to high altitudes and genetic changes due to bottleneck, we assembled the genome (~1.02 Gb) of a white eared pheasant (WT) (Crossoptilon crossoptilon) inhibiting at high altitudes (3,000~7,000 m) in northwest of Yunnan province, China, using a combination of Illumina short reads, PacBio long reads and Hi-C reads, with a contig N50 of 19.63 Mb and only six gaps. To further provide resources for gene annotation as well as functional and population genetics analyses, we sequenced transcriptomes of 20 major tissues of the WT individual and re-sequenced another 10 WT individuals and a blue eared pheasant (Crossoptilon auritum) individual inhabiting at intermediate altitudes (1,500~3,000 m). Our assembled WT genome, transcriptome data, and DNA sequencing data can be valuable resources for studying the biology, evolution and developing conservation strategies of these endangered species.