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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.

Oxidative stress (OS) is a chemical imbalance between an oxidant and an antioxidant, causing damage to redox signaling and control or causing molecular damage. Unbalanced oxidative metabolism can produce excessive reactive oxygen species (ROS). These excess ROS can cause drastic changes in platelet metabolism and further affect platelet function. It will also lead to an increase in platelet procoagulant phenotype and cell apoptosis, which will increase the risk of thrombosis. The creation of ROS and subsequent platelet activation, adhesion, and recruitment are then further encouraged in an auto-amplifying loop by ROS produced from platelets. Meanwhile, cancer cells produce a higher concentration of ROS due to their fast metabolism and high proliferation rate. However, excessive ROS can result in damage to and modification of cellular macromolecules. The formation of cancer and its progression is strongly associated with oxidative stress and the resulting oxidative damage. In addition, platelets are an important part of the tumor microenvironment, and there is a significant cross-communication between platelets and cancer cells. Cancer cells alter the activation status of platelets, their RNA spectrum, proteome, and other properties. The “cloaking” of cancer cells by platelets providing physical protection，avoiding destruction from shear stress and the attack of immune cells, promoting tumor cell invasion.We explored the vicious circle interaction between ROS, platelets, and cancer in this review, and we believe that ROS can play a stimulative role in tumor growth and metastasis through platelets.

Different temperature sensors show different measurement values when excited by the same dynamic temperature source. Therefore, a method is needed to determine the difference between dynamic temperature measurements. This paper proposes a novelty approach to treating dynamic temperature measurements over a period of time as a temperature time series, and derives the formula for the distance between the measurement values using uniformsampling within the time series analysis. The similarity is defined in terms of distance to measure the difference. The distance measures were studied on the analog measurement datasets. The results show that the discrete Fréchet distance has stronger robustness and higher sensitivity. The two methods have also been applied to an experimental dataset. The experimental results also confirm that the discrete Fréchet distance performs better.

Ursodeoxycholic acid (UDCA) is a fundamental treatment drug for numerous hepatobiliary diseases that also has adjuvant therapeutic effects on certain cancers and neurological diseases. Chemical UDCA synthesis is environmentally unfriendly with low yields. Biological UDCA synthesis by free-enzyme catalysis or whole-cell synthesis using inexpensive and readily available chenodeoxycholic acid (CDCA), cholic acid (CA), or lithocholic acid (LCA) as substrates is being developed. The free enzyme-catalyzed one-pot, one-step/two-step method uses hydroxysteroid dehydrogenase (HSDH); whole-cell synthesis, mainly uses engineered bacteria (mainly Escherichia coli) expressing the relevant HSDHs. To further develop these methods, HSDHs with specific coenzyme dependence, high enzyme activity, good stability, and high substrate loading concentration, P450 monooxygenase with C-7 hydroxylation activity and engineered strain harboring HSDHs must be exploited.

As a critical prerequisite for semantic facade reconstruction, accurately separating wall and protrusion points from facade point clouds is required. The performance of traditional separation methods is severely limited by facade conditions, including wall shapes (e.g., nonplanar walls), wall compositions (e.g., walls composed of multiple noncoplanar point clusters), and protrusion structures (e.g., protrusions without regularity, repetitive, or self-symmetric features). This study proposes a more widely applicable wall and protrusion separation method. The major principle underlying the proposed method is to transform the wall and protrusion separation problem as a ground filtering problem and to separate walls and protrusions using ground filtering methods, since the 2 problems can be solved using the same prior knowledge, that is, protrusions (nonground objects) protrude from walls (ground). After transformation problem, cloth simulation filter was used as an example to separate walls and protrusions in 8 facade point clouds with various characteristics. The proposed method was robust to the facade conditions, with a mean intersection over union of 90.7%, and had substantially higher accuracy compared with the traditional separation methods, including region growing-, random sample consensus-, multipass random sample consensus-based, and hybrid methods, with mean intersection over union values of 69.53%, 49.52%, 63.93%, and 47.07%, respectively. Besides, the proposed method was general, since existing ground filtering methods (including the maximum slope, progressive morphology, and progressive triangular irregular network densification filters) can also perform well.

Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R2 = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions.

The Belt and Road (B&R) region, a vital area with historical, economic, cultural and political significance, has undergone rapid urbanization in the past several decades, especially in the form of urban expansion. In this study, 20 megacities in the B&R region were selected to explore different spatiotemporal patterns of urban expansion. Object-oriented support vector machines (SVM), annual growth rate (AGR) models, and landscape metrics were employed to delineate the urban areas and characterize spatiotemporal characteristics and landscape patterns of these megacities during 1975–2015. All urban maps presented high overall accuracies (80.70%–95.90%) and overall Kappa coefficients (0.76–0.95). The study revealed that megacities in the B&R region have undergone different types of urban sprawl, mainly adopting a ‘concentric circle’ pattern in inland areas and a ‘sector’ pattern in coastal areas. Besides, six expansion modes were summarized according to the AGRs of individual megacities. Differences existed in megacities of the developing and developed countries and among five sub-regions. Moreover, ‘dispersion, gathering, and re-dispersion’ and ‘coalescence’ were two major landscape patterns of megacities in developing and developed countries. Results of this study can provide a scientific reference for urban planning and aid in sustainable development of local areas.

Rectal cancer is a malignant tumor with poor prognosis. Identification of prognostic biomarkers is needed to improve overall survival of rectal cancer patients. Here, we firstly identified miR-20a-5p significantly classifying high-risk group and low-risk group of rectal cancer patients. We also found that several known miRNAs miR-142-5p, miR-486-5p, miR-490-3p and miR-133a-3p played important roles in rectal cancer. Secondly, we constructed and analyzed a rectal cancer-related miRNA-mRNA network. A rectal cancer-related functional module was identified from the miRNA-mRNA network. Survival analysis demonstrated great prognosis capacity of the module to distinguish rectal cancer patients. Thirdly, a rectal cancer-related miRNA-lncRNA network was constructed, which followed power law distribution. Hub miRNAs and lncRNAs of the network were suggested to show significant prognosis ability and be enriched in cancer-related pathways. Fourthly, we constructed a rectal cancer-related ceRNA network and detected several typical lncRNA-miRNA-mRNA crosstalk, such as HAND2-AS1, HAND2 and miR-20a-5p crosstalk and MBNL1-AS1, miR-429 and LONRF2 crosstalk, which were validated to function in improving overall survival of rectal cancer patients. Finally, we identified the regulatory feedback that was constituted by transcriptional factors and lncRNAs, including MEIS1, MEIS2 and multiple lncRNAs. We also demonstrated that these lncRNAs were high related to immune cell infiltration. All these results can help us to uncover the molecular mechanism and provide new light on miRNA-mediated gene crosstalks in rectal cancer.

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan–Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein–protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients’ overall survival (log rank p < 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan–Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.

Ground filtering is one of the essential steps for processing airborne light detection and ranging data in forestry applications. However, the performance of existing methods is still limited in forested areas due to the complex terrain and dense vegetation. To overcome this limitation, we proposed an improved surface-based filter based on multi-directional narrow window and cloth simulation. The innovations mainly involve two aspects as follows: (1) sufficient and uniformly distributed ground seeds are identified by merging the lowest points and line segments from the point clouds within a multi-directional narrow window; (2) complete and accurate ground points are extracted using a cyclic scheme that includes incorrect ground point elimination using the internal force adjustment of cloth simulation, terrain reconstruction with moving least-squares plane fitting, and ground point extraction based on progressively refined terrain. The proposed method was tested in five forested sites with various terrain characteristics and vegetation distributions. Experimental results showed that the proposed method could accurately separate ground points from non-ground points in different forested environments, with the average kappa coefficient of 88.51% and total error of 4.22%. Moreover, the comparative experiments proved that the proposed method performed better than the classical methods involving the slope-based, mathematical morphology-based and surface-based methods.

Prompt and precise acknowledgement of surface change around subways is of considerable significance in urban rail protection and local environmental management. Research has proven the considerable potential of synthetic aperture radar (SAR) images for detecting such information; however, previous studies have mostly focused on change intensity using single Difference images (DIs), e.g., difference value DI (DVDI) and mean value DI (MVDI). With the aim of more accurate information with respect to surface changes around subways, in this study, we proposed a novel SAR detection method that involved three steps: (1) the calculation of three single DIs, (2) the combination of the single DIs and (3) the delineation of the changed area. Compared to existing detection methods, the proposed method represents three major improvements. First, both the intensity information and phase information were applied by combining the DVDI, MVDI and coherent difference images (CDIs). Secondly, a local energy weight (LEW) approach was proposed to combine single DIs instead of the normally used equal weights. Because the changed area often comprises continuous rather than discrete pixels, a combined DI with the LEW (“CoDI-LEW” hereafter) fully considers the attributes of adjacent pixels and enhances the signal-to-noise ratio of SAR images. Thirdly, the FCM algorithm, instead of the widely used threshold methods, was applied to distinguish changed areas from unchanged areas. An experimental comparison with several existing detection methods showed that the proposed method could delineate changed areas with higher accuracy in terms of both quality and quantity. Furthermore, it can effectively execute detection under diverse surface change conditions with good feasibility and applicability.

As a large and populous developing country, China has entered the rapid urbanization stage since 2000. Until 2018, China has accounted for nearly 1/5 of global megacities. Understanding their urbanization processes is of great significance. Given the deficiencies of existing research, this study explored the interannual urbanization process of China’s six megacities during 2000−2018 from four aspects, namely, the basic characteristics of urban land expansion, expansion types, cotemporary evolution of urban land−population−economy, and urbanization effects on the local environment. Results indicated that (1) urban lands in China’s six megacities increased by 153.27%, with distinct differences across megacities; (2) all of six megacities experienced the expansion processes from high-speed to low-speed, but they varied greatly in detail; (3) the speeds of urban land expansion in China’s megacities outpaced the population growth but lagged behind in GDP increase; and (4) urbanization has triggered an environmental crisis, which is represented by the decline in vegetation coverage and the increase in land surface temperature in newly expanded urban lands. This study enriched the content of urbanization, supplemented the existing materials of megacities, and provided a scientific reference for designing rational urban planning.

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

Accurate and detailed monitoring of population distribution and evolution is of great significance in formulating a population planning strategy in China. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime lights time-series (NLT) image products offer a good opportunity for detecting the population distribution owing to its high correlation to human activities. However, their detection capability is greatly limited owing to a lack of in-flight calibration. At present, the synergistic use of systematically-corrected NLT products and population spatialization is rarely applied. This work proposed a methodology to improve the application precision and versatility of NLT products, explored a feasible approach to quantitatively spatialize the population to grid units of 1 km × 1 km , and revealed the spatio-temporal characteristics of population distribution from 2000 to 2010. Results indicated that, (1) after inter-calibration, geometric, incompatibility and discontinuity corrections, and adjustment based on vegetation information, the incompatibility and discontinuity of NTL products were successfully solved. Accordingly, detailed actual residential areas and luminance differences between the urban core and the peripheral regions could be obtained. (2) The population spatialization method could effectively acquire population information at per km 2 with high accuracy and exhibit more details in the evolution of population distribution. (3) Obvious differences in spatio-temporal characteristics existed in four economic regions, from the aspects of population distribution and dynamics, as well as population-weighted centroids. The eastern region was the most populous with the largest increased magnitude, followed by the central, northeastern, and western regions. The population-weighted centroids of the eastern, western, and northeastern regions moved along the southwest direction, while the population-weighted centroid of the central region moved along the southeast direction. (4) The population distribution and dynamics in nine-level population density types were significantly different. In the period of 2000–2010, the population in the basic no-man and high concentration types presented a net decrease. The population in seven other regions all increased with a net increase ranging from 25 km 2 (the moderate concentration type) to 245,668 km 2 (the general transition type). Except those in the core concentration and extremely sparse types, the population-weighted centroids in all other population density types moved along the southwest direction.

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.