Sequence Characteristics Research Articles

Characterization of Plant Defensin (PDF) Genes in Banana (Musa acuminata) Reveals the Antifungal Ability of MaPDF2.2 to Fusarium Wilt Pathogens

Plant defensin (PDF/DEF), an important pathogenesis-related protein which widely exists in plants, displays broad-spectrum antifungal activities. To date, however, reports on the banana PDFs are very limited. In this study, we identified, cloned, and characterized the five Class I PDFs (MaPDF2.1~MaPDF2.5) in banana (Musa acuminata). Further, their expression in root, corm, leaf, and fruit were studied. MaPDFs exhibited quite different expression patterns in different organs, with MaPDF2.2 as the only member expressing in all the tested organs, and its expression levels in all organs were the highest among all MaPDFs. The MaPDF2.2 expression could be significantly upregulated by both low- and high-temperature stresses but significantly downregulated by the inoculations of plant growth promoting endophytic fungus Serendipita indica and banana Fusarium wilt (FW) pathogen Fusarium oxysporum f. sp. cubense (Foc) Tropical race 4 (FocTR4). Moreover, the S. indica pre-colonization could significantly alleviate the suppression of FocTR4 on MaPDF2.2, suggesting that this MaPDF might contribute greatly to the S. indica-enhanced FW resistance. By using tobacco leaf transient overexpression, the function of MaPDF2.2 was investigated. Its overexpression significantly inhibited the infection of Foc race 1 (Foc1) and FocTR4 in tobacco leaves. Furthermore, in vitro antifungal ability assays revealed that the recombinant His-MaPDF2.2 protein could significantly inhibit the growth of Foc1 and FocTR4, as well as the pigment accumulation of Foc1. Our study revealed the sequence and expression characteristics of banana PDFs and demonstrated the antifungal ability of MaPDF2.2 to FW pathogens.

Development and application of a whole transcriptome sequencing assay for the detection of gene fusions in clinical cancer specimens

BackgroundGene fusions are an important driver of cancer and require rapid and accurate detection to guide clinical decisions. However, the performance characteristics of whole transcriptome sequencing (WTS) for the detection of gene fusions have not been thoroughly investigated.MethodsWe developed a novel WTS-based assay for the detection of gene fusions, MET exon 14 skipping and EGFR VIII alterations in clinical samples.ResultsWe defined a DV200 value ≥ 30% as the threshold for RNA degradation, RNA input, fusion expression and number of mapped reads greater than 100 ng, 40 copies/ng and 80 Mb for optimal sensitivity of the WTS assay. Our assay successfully identified 62 out of 63 known gene fusions, achieving a sensitivity of 98.4%. The specificity of the assay was 100%, as no fusions were detected in the 21 fusion-negative samples. Good repeatability and reproducibility were observed in replicates, except for the TPM3::NTRK1 fusion, which was expressed below the threshold. Of all fusions identified in 101 NSCLC samples, 68.9% (20/29) were potentially actionable, compared to 20% in pan-cancer samples. In addition to actionable fusions, we also identified many fusions with potential diagnostic and prognostic value in pan-cancer.ConclusionsWe have developed a novel WTS assay with high sensitivity, specificity, repeatability and reproducibility. This assay can identify potentially actionable gene fusions and provides valuable insights into the fusion landscape in various cancers, which may help guide treatment decisions and aid in diagnosis and prognosis.

Highly sensitive and specific markers for detection of mismatch repair deficiency by next-generation sequencing

Abstract Objective To identify exonic markers that could improve analytic performance characteristics of next-generation sequencing (NGS) in detecting mismatch repair deficiency (dMMR) using colorectal cancer (CRC) as a model. Methods Coding sequences of a target NGS panel (~1.13 megabase) were compared between dMMR CRC and mismatch repair–proficient (pMMR) CRC in a training cohort (41 dMMR CRCs and 213 pMMR CRCs) and a validation cohort (33 dMMR CRCs and 307 pMMR CRCs) with documented mismatch repair status by immunohistochemical and/or microsatellite instability assays. Results The dMMR CRC cases showed significantly higher insertion/deletion (indel) mutations within exonic homopolymers (homo-indels), occurring predominantly within longer repeats of 5 to 10 nucleotides (92%, P < .0001), rather than shorter repeats of 2 to 4 nucleotides seen in pMMR CRC (62%). Homo-indels in dMMR CRC were not random. Hotspot loci were consistent between the training and validation cohorts. The dMMR defined by indels within homopolymers of 5 or more nucleotides, homopolymers of 7 or more nucleotides, or a panel of hotspots all showed 100% sensitivity and specificity with a range of cutoffs. Conclusions We propose that this approach allows one to identify highly sensitive and specific markers for detecting dMMR CRC by NGS alone. Further studies are warranted to test whether these markers are applicable to non-CRC neoplasms.

A Multi-Encoder BHTP Autoencoder for Robust Lithium Battery SOH Prediction Under Small-Sample Scenarios

Accurate prediction of the state of health (SOH) in lithium batteries (LiBs) is essential for ensuring operational safety, extending battery lifespan, and enabling effective second-life applications. However, achieving precise SOH prediction under small-sample conditions remains a significant challenge due to inherent variability among battery cells and capacity recovery phenomena, which result in irregular degradation patterns and hinder effective feature extraction. To overcome these challenges, this study introduces an advanced autoencoder-based method specifically designed for SOH prediction in small-sample scenarios. This method employs a multi-encoder structure—comprising token, positional, and temporal encoders—to comprehensively capture the multi-dimensional characteristics of SOH sequences. Furthermore, a BHTP module is integrated to facilitate feature fusion and enhance the model’s stability and interpretability. By utilizing a pre-training and fine-tuning strategy, the proposed method effectively reduces computational complexity and the number of model parameters while maintaining high prediction accuracy. The validation of the NASA 18650 lithium cobalt oxide battery dataset under various discharge strategies shows that the proposed method achieves fast convergence and outperforms traditional prediction methods. Compared with other models, our method reduces the RMSE by 0.004 and the MAE by 0.003 on average. In addition, ablation experiments show that the addition of the multi-encoder structure and the BHTP module improves the RMSE and MAE by 0.008 and 0.007 on average, respectively. These results highlight the robustness and utility of the proposed method in real battery management systems, especially under data-scarce conditions.

Sequence characteristics, evolutionary history and expression pattern of BCO2 in Chinese mitten crab Eriocheir sinensis

Sequence characteristics, evolutionary history and expression pattern of BCO2 in Chinese mitten crab Eriocheir sinensis

Genome-Wide Identification and Classification of Arabinogalactan Proteins Gene Family in Gossypium Species and GhAGP50 Increases Numbers of Epidermal Hairs in Arabidopsis

Arabinogalactan proteins (AGPs) constitute a diverse class of hydroxyproline-rich glycoproteins implicated in various aspects of plant growth and development. However, their functional characterization in cotton (Gossypium spp.) remains limited. As a globally significant economic crop, cotton serves as the primary source of natural fiber, making it essential to understand the genetic mechanisms underlying its growth and development. This study aims to perform a comprehensive genome-wide identification and characterization of the AGP gene family in Gossypium spp., with a particular focus on elucidating their structural features, evolutionary relationships, and functional roles. A genome-wide analysis was conducted to identify AGP genes in Gossypium spp., followed by classification into distinct subfamilies based on sequence characteristics. Protein motif composition, gene structure, and phylogenetic relationships were examined to infer potential functional diversification. Subcellular localization of a key candidate gene, GhAGP50, was determined using fluorescent protein tagging, while gene expression patterns were assessed through β-glucuronidase (GUS) reporter assays. Additionally, hormonal regulation of GhAGP50 was investigated via treatments with methyl jasmonate (MeJA), abscisic acid (ABA), indole-3-acetic acid (IAA), and gibberellin (GA). A total of 220 AGP genes were identified in Gossypium spp., comprising 19 classical AGPs, 28 lysine-rich AGPs, 55 AG peptides, and 118 fasciclin-like AGPs (FLAs). Structural and functional analyses revealed significant variation in gene organization and conserved motifs across subfamilies. Functional characterization of GhAGP50, an ortholog of AGP18 in Arabidopsis thaliana, demonstrated its role in promoting epidermal hair formation in leaves and stalks. Subcellular localization studies indicated that GhAGP50 is targeted to the nucleus and plasma membrane. GUS staining assays revealed broad expression across multiple tissues, including leaves, inflorescences, roots, and stems. Furthermore, hormonal treatment experiments showed that GhAGP50 expression is modulated by MeJA, ABA, IAA, and GA, suggesting its involvement in hormone-mediated developmental processes. This study presents a comprehensive genome-wide analysis of the AGP gene family in cotton, providing new insights into their structural diversity and functional significance. The identification and characterization of GhAGP50 highlight its potential role in epidermal hair formation and hormonal regulation, contributing to a deeper understanding of AGP functions in cotton development. These findings offer a valuable genetic resource for future research aimed at improving cotton growth and fiber quality through targeted genetic manipulation.

Pato: prediction of probiotic bacteria using metabolic features.

Probiotics have gained recognition for their health-promoting benefits, particularly in the gastrointestinal and immunological systems. Among promising probiotic candidates, Lactobacillus strains, belonging to the lactic acid bacteria (LAB) group, play a significant role in human microbiota. To aid in the in silico identification of Lactobacillus strains with probiotic potential, this study presents a novel classification approach based on functional and metabolism-related elements, which offers improved accuracy and explainability compared to traditional k-mer-based methods. By considering the functional characteristics of genomic sequences, this approach contributes to a clearer understanding of the traits associated with probiotic activity, facilitating the selection of strains with optimal health-promoting attributes. The webserver is available at http://200.132.101.156:5001/ .

Digital media use as social practice: Quantifying its temporal characteristics and changes across 16 years, 2003–2018

Digital media use is an essential social practice in modern life. In this study we focus on the patterns and changes of digital media use as indicators of social change. A sequence perspective is adopted to analyze the media use rhythms from 2003 to 2018, considering both whole sequence and temporal characteristics. Results show that the rhythms of predominant use have remained stable at the whole sequence level but have become diversified and fragmented at the temporal characteristic level. Furthermore, different sociodemographic groups have exhibited differentiated rhythms in recent years. The findings highlight the complexity of digital media use changes across time. Within the same period, the occurrence and direction of changes can vary among intrapersonal, interpersonal, and social group perspectives.

Dynamic interchange of local residue–residue interactions in the largely extended single alpha-helix in Drebrin

Single alpha-helices (SAHs) are protein regions with unique mechanical properties, forming long, stable, monomeric helical structures in solution. To date, only a few naturally occurring SAH regions have been extensively characterized, primarily from myosins, leaving the structural and dynamic variability of SAH regions largely unexplored. Drebrin (developmentally regulated brain protein) contains a predicted SAH segment with unique sequence characteristics, including aromatic residues within the SAH region and a preference for arginine over lysine in its C-terminal half. Using circular dichroism (CD) and NMR spectroscopy, combined with small-angle X-ray scattering (SAXS) measurements, we demonstrate that the Drebrin-SAH is helical and monomeric in solution. NMR resonance assignment required specific 4D techniques to resolve severe signal overlap resulting from the low complexity and largely helical conformation of the sequence. To further characterize its structure, we generated a structural ensemble consistent with Cα, Cβ chemical shifts and SAXS data, revealing a primarily extended structure with non-uniform helicity. Our results suggest that dynamic rearrangement of salt bridges and potential transient cation-π interactions contribute to the formation and stabilization of both helical and non-helical local conformational states.

Functional analysis of the strawberry FaPR1 gene in response to Colletotrichum gloeosporioides infection

Currently, the PR1 gene in strawberry has not been reported yet. Therefore, in this study, the FaPR1 gene sequence was obtained and cloned based on the transcriptome data of strawberry in response to anthracnose infection. The characteristics of FaPR1-related sequences were analyzed using bioinformatics methods. Moreover, the potential interacting proteins of FaPR1 were predicted using the interaction pairs included in the STRING database, and the interaction between them was verified by the yeast two-hybrid method. In addition, the phenotypic and physiological changes of strawberry materials with overexpressed FaPR1 gene after infection by Colletotrichum gloeosporioides (Penz.) Sacc. were analyzed to provide reference genes and a theoretical basis for molecular breeding for anthracnose resistance. The main results were as follows: (1) The FaPR1 gene encodes a protein containing 162 amino acid residues with an isoelectric point of 5.52; (2) The FaPR1 protein is closely related to Rosa chinensis Jacq. PR1 (RcPR1) protein and Rosa hugonis Hemsl. PR1 (RhPR1) protein; (3) There is an interaction between FaPR1 and the strawberry thaumatin-like protein 1 (FaTLP1); (4) The FaPR1 gene plays an important role in strawberry resistance to anthracnose, and overexpression of the FaPR1 gene can enhance strawberry resistance to this disease.

Genome-wide identification of TRAF family genes in Urechis unicinctus (Annelida, Echiura) and their responses in resistance to high temperature stress.

Genome-wide identification of TRAF family genes in Urechis unicinctus (Annelida, Echiura) and their responses in resistance to high temperature stress.

An efficient parallel runoff forecasting model for capturing global and local feature information

Artificial intelligence has significantly accelerated the development of hydrological forecasting. However, research on how to efficiently identify the physical characteristics of runoff sequences and develop forecasting models that simultaneously address both global and local features of the sequences is still lacking. To address these issues, this study proposes a new PCPFN (PolyCyclic Parallel Fusion Network) prediction model that leverages the multi-periodic characteristics of runoff sequences and shares global features through a dual-architecture parallel computation approach. Unlike existing models, the PCPFN model can extract both the periodic and trend-based evolution features of runoff sequences. It constructs a multi-feature set in a “sequence-to-sequence” manner and employs a parallel structure of an Encoder and BiGRU (Bidirectional Gated Recurrent Unit) to simultaneously capture changes in both local, adjacent features and global characteristics, ensuring comprehensive attention to the sequence features. When predicting runoff data for three different hydrological conditions, the PCPFN model achieved R2 values of 0.97, 0.98, and 0.97, respectively, with other evaluation indicators significantly outperforming the benchmark models. Additionally, due to the opacity in feature distribution processes of AI models, SHAP (Shapley Additive exPlanations) analysis was used to evaluate the contribution of each feature variable to long-term runoff trends. The proposed PCPFN model, during parallel computation, not only utilizes the intrinsic features of sequences and efficiently handles the distribution of local and global features but also shares predictive information in the output module, achieving accurate runoff forecasting and providing crucial references for timely warning and forecasting.

PhysDiff: Physiology-based Dynamicity Disentangled Diffusion Model for Remote Physiological Measurement

Recent works on remote PhotoPlethysmoGraphy (rPPG) estimation typically use techniques like CNNs and Transformers to encode implicit features from facial videos for prediction. These methods learn to directly map facial videos to the static values of rPPG signals, overlooking the inherent dynamic characteristics of rPPG sequence. Moreover, the rPPG signal is extremely weak and highly susceptible to interference from various sources of noise, including illumination conditions, head movements, and variations in skin tone. To address these limitations, we propose a Physiology-based dynamicity disentangled diffusion (PhysDiff) model particularly designed for robust rPPG estimation. PhysDiff leverages the diffusion model to learn the distribution of quasi-periodic rPPG signal and uses a dynamicity disentanglement strategy to capture two dynamic characteristics in temporal rPPG signal, i.e., trend and amplitude. This disentanglement is motivated by the underlying dynamic physiological processes of vasodilation and vasoconstriction, ensuring a more precise representation of the rPPG signal. The disentangled components are then used as pivotal conditions in the proposed spatial-temporal hybrid denoiser for rPPG reconstruction. Besides, we introduce a periodicity-based multi-hypothesis selection strategy in model inference, which compares the natural periodicity of multiple generated rPPG hypotheses and selects the most favorable one as the final prediction. Extensive experiments on four datasets demonstrate that our PhysDiff significantly outperforms prior methods on both intra-dataset and cross-dataset testing.

SIGMAR1 screened by a GPCR-related classifier regulates endoplasmic reticulum stress in bladder cancer

BackgroundBladder cancer (BC) is one of the most common malignancies worldwide. G protein-coupled receptors (GPCRs) are a large family of transmembrane proteins that are increasingly recognised as key players in cancer biology, affecting cell signalling and the tumour microenvironment. The sigma-1 receptor (SIGMAR1), although not a classical GPCR, has similar functions and is associated with the regulation of ER stress. However, its specific role and mechanism in bladder cancer are still unclear.MethodThe data sets pertaining to batch sequencing, single-cell RNA sequencing (scRNA-seq), immunotherapy response and clinical pathological characteristics were obtained from the public database. Thereafter, multiple algorithms were employed for the screening of GPCRs and immune cells related to the prognosis of BC. A GPCR-tumour microenvironment (TME) classifier was constructed and validated using different queues and multi-omics methods. The key biological pathways between GPCR-TME subgroups were identified through the utilisation of methodologies such as Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA), and Tumour Immunophenotype Tracking (TIP). The expression of SIGMAR1 in BC cell lines and tissue samples was validated by western blotting. The Gene Ontology (GO) and GSEA were employed for biological process enrichment analysis. The biological role of SIGMAR1 in BC was investigated through functional experiments and subcutaneous tumour-bearing experiments in nude mice. The relationship between SIGMAR1 and immune cell infiltration was explored using the CIBERSORT method.ResultsA total of 15 types of GPCR and 5 types of immune cells were identified and established as a GPCR-TME classifier. Patients in the GPCR-low + TME-high group exhibited the most favourable prognosis, whereas patients in the GPCR-high + TME-low group demonstrated the least favourable prognosis. The scRNA-seq results revealed an increase in GPCR expression in CD8 + T cells, endothelial cells, and NK cells. GPCR-TME was significantly correlated with overall survival (OS) in BC patients and outperformed a range of clinical parameters, making it an independent risk factor affecting the prognosis of BC patients. In comparison to normal tissues, SIGMAR1 was markedly expressed in BC tissues, and was associated with a poor prognosis. Functional experiments demonstrated that SIGMAR1 deficiency impeded the invasive capacity of cancer cells and restrained cellular proliferation. Moreover, in vivo experiments corroborated that SIGMAR1 deficiency curtailed the growth of xenografts in nude mice. Western blotting analysis revealed that SIGMAR1 silencing intensified endoplasmic reticulum (ER) stress in BC cells and promoted cell apoptosis. Additionally, the expression level of SIGMAR1 was correlated with the level of immune cell infiltration and immune-related functions.ConclusionThe construction of a BC-related GPCR-TME classifier enabled the effective prediction of the OS of BC patients and the identification of SIGMAR1, a key factor regulating ER stress in BC. The knockout of SIGMAR1 can destroy its protective effect on ER stress, enhance apoptosis of BC cells, and facilitate further investigation of novel treatment strategies for cancer therapy.

Energy and directionality characteristics of seismic sequences and typical bridge response in the Ms6.4 Yangbi earthquake of May 21, 2021

Energy and directionality characteristics of seismic sequences and typical bridge response in the Ms6.4 Yangbi earthquake of May 21, 2021

Numerical calculation method for the probability of target damage caused by fragment swarm under random retention time series

Addressing the issue of uncertainty in calculating the damage effectiveness of aerial maneuvering targets due to the random intersection with warhead fragment clusters formed by projectile proximity explosions, this paper puts forward a numerical calculation method of the aerial maneuvering target damage probability under the intersection between the warhead fragment group and the target with random dwell time sequence (RDTS) characteristics. Leveraging the random scattering characteristics of warhead fragment clusters and incorporating the vulnerability characteristics of the aerial maneuvering target, a model for calculating the probability of hitting and damaging an aerial maneuvering target with a warhead fragment cluster from a single projectile’s explosive is established. Factors related to the random dwell time of the target possibly falling into the effective damage area of the warhead fragment cluster from the projectile’s proximity explosion are introduced, resulting in the development of a model for calculating the damage probability of an aerial maneuvering target under RDTS. In addition, we provide a mathematical description of the statistical characteristics of the probability of damage. Through simulation and example calculation and analysis, the results demonstrate that this method can accurately reflect the actual damage effectiveness of warhead fragment clusters formed by projectile proximity explosion on the aerial maneuvering target in a random scattering state, offering a novel theoretical approach for calculating the damage effectiveness of highly maneuverable targets in the future.

Phylogenomics resolve the systematics and biogeography of the ant tribe Myrmicini and tribal relationships within the hyperdiverse ant subfamily Myrmicinae.

Ants are a globally distributed and highly diverse group of eusocial animals, playing key ecological roles in most of the world's terrestrial ecosystems. Our understanding of the processes involved in the evolution of this diverse family is contingent upon our knowledge of the phylogeny of the ants. While relationships among most subfamilies have come into resolution recently, several of the tribal relationships within the hyperdiverse subfamily Myrmicinae persistently conflict between or within studies, mirroring the controversial relationships of the Leptanillinae and Martialinae to the remaining ant subfamilies. Another persistent issue of debate in ant phylogenetics is the timing of major evolutionary events as inferred via divergence dating. Here, we test the topology of the myrmicine tribes using genome scale data, inspect gene tree-species tree concordance, and use posterior predictive checks and tests of compositional heterogeneity to infer sequence characteristics which potentially introduce systematic bias in myrmicine tribal topology. Furthermore, we test the placement of the Baltic amber fossil †Manica andrannae by integrating phylogenomic and morphological data from nearly all species within the genus Manica, and a broad sampling of its sister genus Myrmica. Subsequently, we demonstrate the effect of fossil placement on overall divergence times in the Myrmicinae. We then re-evaluate the historical biogeography of the Myrmicini and Pogonomyrmecini considering newly generated genetic data and insights from our phylogenomic results. We find that our current understanding of tribal topology in the Myrmicinae is strongly supported, but this topology is highly sensitive to compositional heterogeneity and gene-tree species-tree conflict. Our fossil placement analyses strongly suggest that †Manica andrannae is a stem Manica species, and analysis of this fossil in context of the Myrmicinae demonstrates that a single fossil calibration point can have broad-scale cascading effects on divergence dates within the entire subfamily. The results of our biogeographic reconstructions indicate a South American origin for the Pogonomyrmecini + Myrmicini. Additionally, our results suggest that the MRCA of Myrmica may have inhabited the western Nearctic in the early Miocene prior to repeated dispersal across Beringia throughout the Miocene and Pliocene. The MRCA of Manica, on the other hand, was inferred to have a Holarctic range prior to vicariance during the Pliocene. Unexpectedly, we found strong support in the Pogonomyrmecini for three coordinated dispersal events from South to Central America during the early Miocene, which has been previously proposed as an early biotic interchange event prior to the more commonly accepted 3.5 Ma closure of the Isthmus of Panama.

A multimodal model for protein function prediction

Protein function, which is determined by sequence, structure, and other characteristics, plays a crucial role in an organism’s performance. Existing protein function prediction methods mainly rely on sequence data and often ignore structural properties that are crucial for accurate prediction. Protein structure provides richer spatial and functional insights, which can significantly improve prediction accuracy. In this work, we propose a multi-modal protein function prediction model (MMPFP) that integrates protein sequence and structure information through the use of GCN, CNN, and Transformer models. We validate the model using the PDBest dataset, demonstrating that MMPFP outperforms traditional single-modal models in the molecular function (MF), biological process (BP), and cellular component (CC) prediction tasks. Specifically, MMPFP achieved AUPR scores of 0.693, 0.355, and 0.478; scores of 0.752, 0.629, and 0.691; and scores of 0.336, 0.488, and 0.459, showing a 3–5% improvement over single-modal models. Additionally, ablation studies confirm the effectiveness of the Transformer module within the GCN branch, further validating MMPFP’s superior performance over existing methods. This multi-modal approach offers a more accurate and comprehensive framework for protein function prediction, addressing key limitations of current models.

Sedimentary Characteristics of the Sandstone Intervals in the Fourth Member of Triassic Akekule Formation, Tarim Basin: Implications for Petroleum Exploration

The fourth member of the Triassic in the Tahe Oilfield, as one of the key strata for clastic rock reservoirs, poses significant challenges to oil and gas exploration due to unclear identification of its depositional environments and sedimentary microfacies. Based on the guidance of sequence stratigraphy and sedimentological theories, this study comprehensively analyzed well logging data from more than 130 wells, core analysis from 9 coring wells (including lithology, sedimentary structures, and facies sequence characteristics), 3D seismic data (covering an area of 360 km2), and regional geological background. Combined with screening and settling method granularity experiments, the sedimentary characteristics of the sand body in the fourth member were systematically characterized. The results indicate the following: (1) In the Tahe Oilfield, the strata within the fourth member of the Triassic are predominantly characterized by marginal lacustrine subfacies deposits, with delta-front subfacies deposits developing in localized areas. (2) From the planar distribution perspective, influenced by the northwestern provenance, a small deltaic depositional system developed in the early stage of the fourth member in the northwestern part of the Triassic Akekule Formation. This system was dominated by subaqueous distributary channel sand bodies, which were subjected to erosion and reshaping by lake water, leading to the formation of several stable sand bars along the lake shoreline. In the later stage of the fourth member, as the lake level continued to recede, the area of deltaic deposition expanded westward, and deltaic deposits also developed in the central to slightly eastern parts of the study area. Based on this, a depositional model for the fourth member of the Triassic in the Tahe Oilfield has been established. (3) In the Tahe Oilfield, the sand bodies within the fourth member of the Triassic system gradually pinch out into mudstone, forming lithological pinch-out traps. Among these, the channel sand bodies and long belt sand ridges, due to their good sorting and high permeability, become favorable reservoirs for oil and gas accumulation. This study clarifies the sedimentary model of the fourth member and reveals the spatial differentiation mechanism of sand bodies under the control of lake-level fluctuations and ancient structures. It can provide exploration guidance for delta lake sedimentary systems similar to the edge of foreland basins, especially for efficient development of complex lithological oil and gas reservoirs controlled by multistage lake invasion–lake retreat cycles.

The complete mitochondrial genome analysis of Haemaphysalis hystricis Supino, 1897 (Ixodida: Ixodidae) and its phylogenetic implications.

In order to study the sequence characteristics, gene order, and codon usage of the mitochondrial genome of Haemaphysalis hystricis, and to explore its phylogenetic relationship, a total of 36 H. hystricis isolated from dogs were used as sample in this study. The mitochondrial genome of a H. hystricis was amplified with several pairs of specific primers by PCR, and was sequenced by first generation sequencing. The mitochondrial genome of H. hystricis was 14,719 bp in size, and it contained 37 genes including 13 protein coding genes (PCGs), 22 transfer RNA genes (tRNAs), 2 ribosomal RNA genes (rRNAs), and AT-rich region. Each PCG sequence had different lengths, the sequence longest and shortest gene were nad5 (1,652 bp) and atp8 (155 bp), respectively, among the 13 PCGs. All PCGs used ATN as their initiation codon, 10 of 13 PCGs used TAN as their termination codon, and 3 of which had incomplete termination codon (TA/T). Most of the 22 tRNAs with different sizes could form the classical cloverleaf structures expect for tRNA-Ala, tRNA-Ser1, tRNA-Ser2, and tRNA-Glu, and there were base mismatch (U-U and U-G) in all the 22 tRNAs sequences. Two rRNAs, namely rrnL and rrnS, had different lengths, rrnL located between tRNA-Leu1 and tRNA-Val, and rrnS located between tRNA-Val and tRNA-Ile, respectively. Two AT (D-loop) control areas with different lengths were in the mitochondrial genome, the NCRL was located between tRNA-Leu2 and tRNA-Cys, and the NCRS was located between rrnS and tRNA-Ile. The complete mitochondrial genome sequence of H. hystricis was AT preferences, and the gene order is the same as that of other Haemaphysalis family ticks. However, phylogenetic analysis showed that H. hystricis was most closely related to Haemaphysalis longicornis among the selected ticks. The mitochondrial genome not only enriches the genome database, provides more novel genetic markers for identifying tick species, and studying its molecular epidemiology, population genetics, systematics, but also have implications for the diagnosis, prevention, and control of ticks and tick-borne diseases in animals and humans.