Long divergent haplotypes introgressed from wild sheep are associated with distinct morphological and adaptive characteristics in domestic sheep.

The worldwide sheep population comprises more than 1000 breeds. Together, these exhibit a considerable morphological diversity, which has not been extensively investigated at the molecular level. Here, we analyze whole-genome sequencing individuals of 1,098 domestic sheep from 154 breeds, and 69 wild sheep from seven Ovis species. On average, we detected 6.8%, 1.0% and 0.2% introgressed sequence in domestic sheep originating from Iranian mouflon, urial and argali, respectively, with rare introgressions from other wild species. Interestingly, several introgressed haplotypes contributed to the morphological differentiations across sheep breeds, such as a RXFP2 haplotype from Iranian mouflon conferring the spiral horn trait, a MSRB3 haplotype from argali strongly associated with ear morphology, and a VPS13B haplotype probably originating from urial and mouflon possibly associated with facial traits. Our results reveal that introgression events from wild Ovis species contributed to the high rate of morphological differentiation in sheep breeds, but also to individual variation within breeds. We propose that long divergent haplotypes are a ubiquitous source of phenotypic variation that allows adaptation to a variable environment, and that these remain intact in the receiving population probably due to reduced recombination.

Genetic diversity and signatures of selection in BoHuai goat revealed by whole-genome sequencing.

BACKGROUND: Cross breeding is an important way to improve livestock performance. As an important livestock and poultry resource in Henan Province of China, Bohuai goat was formed by crossing Boer goat and Huai goat. After more than 20 years of breeding, BoHuai goats showed many advantages, such as fast growth, good reproductive performance, and high meat yield. In order to better develop and protect Bohuai goats, we sequenced the whole genomes of 30 BoHuai goats and 5 Huai goats to analyze the genetic diversity, population structure and genomic regions under selection of BoHuai goat. Furthermore, we used 126 published genomes of world-wide goat to characterize the genomic variation of BoHuai goat. RESULTS: The results showed that the nucleotide diversity of BoHuai goats was lower and the degree of linkage imbalance was higher than that of other breeds. The analysis of population structure showed that BoHuai goats have obvious differences from other goat breeds. In addition, the BoHuai goat is more closely related to the Boer goat than the Huai goat and is highly similar to the Boer goat. Group by selection signal in the BoHuai goat study, we found that one region on chromosome 7 shows a very strong selection signal, which suggests that it could well be the segment region under the intense artificial selection results. Through selective sweeps, we detected some genes related to important traits such as lipid metabolism (LDLR, STAR, ANGPTL8), fertility (STAR), and disease resistance (CD274, DHPS, PDCD1LG2). CONCLUSION: In this paper, we elucidated the genomic variation, ancestry composition, and selective signals related to important economic traits in BoHuai goats. Our studies on the genome of BoHuai goats will not only help to understand the characteristics of the crossbred but also provide a basis for the improvement of cross-breeding programs.

De Novo Assembly of 20 Chicken Genomes Reveals the Undetectable Phenomenon for Thousands of Core Genes on Microchromosomes and Subtelomeric Regions.

The gene numbers and evolutionary rates of birds were assumed to be much lower than those of mammals, which is in sharp contrast to the huge species number and morphological diversity of birds. It is, therefore, necessary to construct a complete avian genome and analyze its evolution. We constructed a chicken pan-genome from 20 de novo assembled genomes with high sequencing depth, and identified 1,335 protein-coding genes and 3,011 long noncoding RNAs not found in GRCg6a. The majority of these novel genes were detected across most individuals of the examined transcriptomes but were seldomly measured in each of the DNA sequencing data regardless of Illumina or PacBio technology. Furthermore, different from previous pan-genome models, most of these novel genes were overrepresented on chromosomal subtelomeric regions and microchromosomes, surrounded by extremely high proportions of tandem repeats, which strongly blocks DNA sequencing. These hidden genes were proved to be shared by all chicken genomes, included many housekeeping genes, and enriched in immune pathways. Comparative genomics revealed the novel genes had 3-fold elevated substitution rates than known ones, updating the knowledge about evolutionary rates in birds. Our study provides a framework for constructing a better chicken genome, which will contribute toward the understanding of avian evolution and the improvement of poultry breeding.

Genome-wide association study for wattles trait in the dairy goat breed.

Dairy goats are significant livestock that provide high-quality milk sources in the world. The wattles trait is an evident phenotypic character on the neck of a dairy goat, which is considered to be under genetic control. We collected samples of 189 dairy goats, including 94 with wattles and 95 without wattles, from four different farms and multiple dairy goat breeds. The samples were genotyped with the GeneSeek Genomic Profiler Goat 70 K SNP chip. Genome-wide association studies (GWAS) in wattles have identified associations with single nucleotide polymorphisms (SNPs) at chromosome 10. In this area, an extremely strong association locus was assigned to FMN1 (Formin 1) belongs to the formin homology family and is associated with limb deformity, other candidate genes of interest confirmed for wattles were ARHGAP11A (Rho GTPase Activating Protein 11 A) and GJD2 (Gap Junction Protein Delta 2). Meanwhile, we found the presence or absence of wattles had no significant effect on milk yield. This research will provide genetic resources useful to explore genetic factors affecting the trait.

Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network.

Objectives: This study proposed an outcome prediction method to improve the accuracy and efficacy of ischemic stroke outcome prediction based on the diversity of whole brain features, without using basic information about patients and image features in lesions. Design: In this study, we directly extracted dynamic radiomics features (DRFs) from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and further extracted static radiomics features (SRFs) and static encoding features (SEFs) from the minimum intensity projection (MinIP) map, which was generated from the time dimension of DSC-PWI images. After selecting whole brain features Ffuse from the combinations of DRFs, SRFs, and SEFs by the Lasso algorithm, various machine and deep learning models were used to evaluate the role of Ffuse in predicting stroke outcomes. Results: The experimental results show that the feature Ffuse generated from DRFs, SRFs, and SEFs (Resnet 18) outperformed other single and combination features and achieved the best mean score of 0.971 both on machine learning models and deep learning models and the 95% CI were (0.703, 0.877) and (0.92, 0.983), respectively. Besides, the deep learning models generally performed better than the machine learning models. Conclusion: The method used in our study can achieve an accurate assessment of stroke outcomes without segmentation of ischemic lesions, which is of great significance for rapid, efficient, and accurate clinical stroke treatment.

A preliminary study of dynamic interactive simulation and computational CT scan of the ideal alveolus model.

BACKGROUND: While the development of CT imaging technique has brought cognition of in vivo organs, the resolution of CT images and their static characteristics have gradually become barriers of microscopic tissue research. PURPOSE: Previous research used the finite element method to study the airflow and gas exchange in the alveolus and acinar to show the fate of inhaled aerosols and studied the diffusive, convective, and sedimentation mechanisms. Our study combines these techniques with CT scan simulation to study the mechanisms of respiratory movement and its imaging appearance. METHODS: We use 3D fluid-structure interaction simulation to study the movement of an ideal alveolus under regular and forced breathing situations and ill alveoli with different tissue elasticities. Additionally, we use the Monte Carlo algorithm within the OpenGATE platform to simulate the computational CT images of the dynamic process with different designated resolutions. The resolutions show the relationship between the kinematic model of the human alveolus and its imaging appearance. RESULTS: The results show that the alveolus and the wall thickness can be seen with an image resolution smaller than 15.6 µm. With ordinary CT resolution, the alveolus is expressed with four voxels. CONCLUSIONS: This is a preliminary study concerning the imaging appearance of the dynamic alveolus model. This technique will be used to study the imaging appearance of the dynamic bronchial tree and the lung lobe models in the future.

Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging.

Introduction: In neurological diagnostics, accurate detection and segmentation of brain lesions is crucial. Identifying these lesions is challenging due to its complex morphology, especially when using traditional methods. Conventional methods are either computationally demanding with a marginal impact/enhancement or sacrifice fine details for computational efficiency. Therefore, balancing performance and precision in compute-intensive medical imaging remains a hot research topic. Methods: We introduce a novel encoder-decoder network architecture named the Adaptive Feature Medical Segmentation Network (AFMS-Net) with two encoder variants: the Single Adaptive Encoder Block (SAEB) and the Dual Adaptive Encoder Block (DAEB). A squeeze-and-excite mechanism is employed in SAEB to identify significant data while disregarding peripheral details. This approach is best suited for scenarios requiring quick and efficient segmentation, with an emphasis on identifying key lesion areas. In contrast, the DAEB utilizes an advanced channel spatial attention strategy for fine-grained delineation and multiple-class classifications. Additionally, both architectures incorporate a Segmentation Path (SegPath) module between the encoder and decoder, refining segmentation, enhancing feature extraction, and improving model performance and stability. Results: AFMS-Net demonstrates exceptional performance across several notable datasets, including BRATs 2021, ATLAS 2021, and ISLES 2022. Its design aims to construct a lightweight architecture capable of handling complex segmentation challenges with high precision. Discussion: The proposed AFMS-Net addresses the critical balance issue between performance and computational efficiency in the segmentation of brain lesions. By introducing two tailored encoder variants, the network adapts to varying requirements of speed and feature. This approach not only advances the state-of-the-art in lesion segmentation but also provides a scalable framework for future research in medical image processing.

Determining acute ischemic stroke onset time using machine learning and radiomics features of infarct lesions and whole brain.

Accurate determination of the onset time in acute ischemic stroke (AIS) patients helps to formulate more beneficial treatment plans and plays a vital role in the recovery of patients. Considering that the whole brain may contain some critical information, we combined the Radiomics features of infarct lesions and whole brain to improve the prediction accuracy. First, the radiomics features of infarct lesions and whole brain were separately calculated using apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences of AIS patients with clear onset time. Then, the least absolute shrinkage and selection operator (Lasso) was used to select features. Four experimental groups were generated according to combination strategies: Features in infarct lesions (IL), features in whole brain (WB), direct combination of them (IW) and Lasso selection again after direct combination (IWS), which were used to evaluate the predictive performance. The results of ten-fold cross-validation showed that IWS achieved the best AUC of 0.904, which improved by 13.5% compared with IL (0.769), by 18.7% compared with WB (0.717) and 4.2% compared with IW (0.862). In conclusion, combining infarct lesions and whole brain features from multiple sequences can further improve the accuracy of AIS onset time.

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

Exploring the Effectiveness of Multi-Objective Training for Organ Substructure Segmentation in Medical Imaging.

Chronic obstructive pulmonary disease (COPD) is closely related to the right ventricle and lung lobes. This study focuses on the segmentation of the right ventricle and lung lobes. We conducted experiments using the MMWHS and our lung lobe datasets and evaluated the segmentation using different training models. We observed that the multi-objective segmentation approach has advantages over single-objective segmentation in segmenting the right ventricle and lung lobes. For the segmentation of the right ventricle, the multi-objective segmentation approach yielded an improvement of 2.0% in the Dice coefficient and 2.5% in the Jaccard index compared to single-objective segmentation. For the segmentation of five lung lobes, the multi-objective segmentation outperformed the single-objective segmentation with Dice coefficient improvements of 1.4%, 1.0%, 1.5%, 0.7%, and 1.3%, respectively.

Intra-cervical lymphatic immunotherapy for dust mite-induced allergic rhinoconjunctivitis in children: a 3-year prospective randomized controlled trial.

Background: Pediatric allergic rhinoconjunctivitis has become a public concern with an increasing incidence year by year. Conventional subcutaneous immunotherapy (SCIT) has long treatment time, high cost and poor compliance. The novel immunotherapy significantly shortens the course of treatment by directly injecting allergens into cervical lymph nodes, which can perform faster clinical benefits to children. Objective: By comparing with SCIT, this study aimed to evaluate the long-term efficacy and safety of intra-cervical lymphatic immunotherapy (ICLIT). Methods: This is a prospective randomized controlled study. A total of 50 allergic rhinoconjunctivitis children with dust mite allergy was randomly divided into ICLIT group and SCIT group, receiving three cervical intralymphatic injections of dust mite allergen or three years of subcutaneous injection, separately. Primary outcomes included total nasal symptom scores (TNSS), total ocular symptom scores (TOSS), total symptom scores (TSS), total medication scores (TMS), and total quality of life score. Secondary outcomes included pain perception and adverse reactions during treatment. Other secondary outcome was change in Dermatophagoides pteronyssinus (Derp) and Dermatophagoides farina (Derf) -specific IgE level. Results: Both groups had significantly decreased TNSS, TOSS, TSS, TMS, and total quality of life score after 36 months of treatment (p<0.0001). Compared with SCIT, ICLIT could rapidly improve allergic symptoms (p<0.0001). The short-term efficacy was consistent between the two groups (p=0.07), while the long-term efficacy was better in SCIT group (p<0.0001). The pain perception in ICLIT group was lower than that in SCIT group (p<0.0001). ICLIT group was safer. Specifically, the children had only 3 mild local adverse reactions without systemic adverse reactions. The SCIT group had 14 systemic adverse reactions. At last, the serum Derp and Derf-specific IgE levels in ICLIT and SCIT groups decreased 3 years later (p<0.0001). Conclusion: ICLIT could ameliorate significantly the allergic symptoms in pediatric patients with an advantage in effectiveness and safety, besides an improved life quality including shortened period of treatment, frequency of drug use and pain perception. Clinical trial registration: https://www.chictr.org.cn/, identifier ChiCTR1800017130.

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution.

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse attention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edged features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy reduces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation techniques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway structures. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare professionals.

A case report of phacolytic glaucoma in lens cortex behind posterior capsule: A CARE-compliant article.

RATIONALE: Phacolytic glaucoma (PLG), a secondary open-angle glaucoma caused by high molecular weight proteins leaking through the capsule of a hypermature cataract. Leakage of liquefied lens cortex behind the posterior capsule is rare. In this paper, we review a case of phacolytic glaucoma in the lens cortex behind posterior capsule. PATIENT CONCERNS: This case report describes a 79-year-old male patient with a 7-year history of progressive blurred vision and a 1-day history of distended in his left eye. He underwent phacoemulsification combined with intraocular lens implantation at our facility 7 years ago. DIAGNOSES: The patient had lower vision (light perception vision) and increased intraocular pressure (IOP) (60 mmHg) in the left eye. Auxiliary inspection found that the left eye had deep anterior chamber depth (around 1 corneal thickness of the peripheral AC angle) as well as vitreous and aqueous humor opacity in the left eye. Combining the clinical symptoms and examinations, we made the diagnosis of PLG in the left eye. INTERVENTIONS: The patient underwent trabeculectomy and extracapsular cataract extraction of the left after a stable ocular condition, during the operation to see that white chyous cortex was visible under the posterior capsule and posterior capsule membrane of the lens was avulsed circularly. OUTCOMES: The postoperative condition was stable. During the follow up of 3 months, the IOP of the left eye was stable without ocular discomfort. LESSONS: This case reported a patient with phacolytic glaucoma in the lens cortex behind posterior capsule who underwent successful surgery, indicating spontaneous capsule rupture can occur in the posterior capsules in PLG and when this situation is detected during the operation, the posterior capsule tearing method can be applied to absorb the lens cortex sticking at the posterior surface of the posterior capsule.

Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification.

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.

Design and characterization of a high-resolution multiple-SNP capture array by target sequencing for sheep.

The efficiency of molecular breeding largely depends on inexpensive genotyping arrays. In this study, we aimed to develop an ovine high-resolution multiple-single-nucleotide polymorphism (SNP) capture array, based on genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology. All the markers were from 40K captured regions, including genes located within selective sweep regions, breed-specific regions, quantitative trait loci (QTL), and the potential functional SNPs on the sheep genome. The results showed that a total of 210K high-quality SNPs were identified in the 40K regions, indicating a high average capture ratio (99.7%) for the target genomic regions. Using genotyped data (n = 317) from liquid chip technology, we further performed genome-wide association studies (GWAS) to detect the genetic loci affecting sheep hair types and teat number. A single significant association signal for hair types was identified on 6.7-7.1 Mb of chromosome 25. The IRF2BP2 gene (chr25: 7,067,974-7,071,785), which is located within this genomic region, has been previously known to be involved in hair/wool traits in sheep. The results further showed a new candidate region around 26.4 Mb of chromosome 13, between the ARHGAP21 and KIAA1217 genes, that was significantly related to teat number in sheep. The haplotype patterns of this region also showed differences in animals with 2, 3, or 4 teats. Advances in using the high-accuracy and low-cost liquid chip are expected to accelerate sheep genomic and breeding studies in the coming years.

Large-scale genotyping platforms are valuable tools for animal selection and breeding programs. The bead chip has been widely used in both research and commercial applications for a long time. A highly efficient and economical genotyping platform has been developed recently. In the present study, by combining the advantages of resequencing and bead chips, we developed a high-resolution capture array based on target sequencing with capture-in-solution technology (liquid chip), including updated functional probes according to the latest research. We further evaluated this approach by using 317 individuals and found that 210K single-nucleotide polymorphisms can be accurately genotyped, confirming the ratio of the captured regions compared with the designed rations is around 99.7%. Genome-wide association studies conducted using this chip suggested IRF2BP2 gene may be involved in hair types and ARHGAP21-KIAA1217 locus may be related to teats number. The liquid chip with high accuracy and low cost can be widely used in genome-wide association studies and genome selection, supporting efforts in molecular breeding and genetic improvement of sheep.

New insights into the novel sequences of the chicken pan-genome by liquid chip.

Increasing evidence indicates that the missing sequences and genes in the chicken reference genome are involved in many crucial biological pathways, including metabolism and immunity. The low detection rate of novel sequences by resequencing hindered the acquisition of these sequences and the exploration of the relationship between new genes and economic traits. To improve the capture ratio of novel sequences, a 48K liquid chip including 25K from the reference sequence and 23K from the novel sequence was designed. The assay was tested on a panel of 218 animals from 5 chicken breeds. The average capture ratio of the reference sequence was 99.55%, and the average sequencing depth of the target sites was approximately 187X, indicating a good performance and successful application of liquid chips in farm animals. For the target region in the novel sequence, the average capture ratio was 33.15% and the average sequencing depth of target sites was approximately 60X, both of which were higher than that of resequencing. However, the different capture ratios and capture regions among varieties and individuals proved the difficulty of capturing these regions with complex structures. After genotyping, GWAS showed variations in novel sequences potentially relevant to immune-related traits. For example, a SNP close to the differentiation of lymphocyte-related gene IGHV3-23-like was associated with the H/L ratio. These results suggest that targeted capture sequencing is a preferred method to capture these sequences with complex structures and genes potentially associated with immune-related traits.

A total of 48K target sites were selected to be placed on the liquid chip, including 23K from the novel sequence of the chicken pan-genome. The high average capture ratio (99.55%) of the reference sequence in five populations indicated the good performance of the liquid chip. For the target region in the novel sequence, the average capture ratio was approximately 33.15% and the average sequencing depth of target sites was approximately 60X, both of which were higher than that of resequencing. However, the capture ratio was different among varieties, ranging from 29.2% (White Leghorn) to 33.4% (B line). GWAS (Genome-wide association study) showed variations in novel sequences potentially related to immune-related traits. For example, an SNP (single nucleotide polymorphism) close to the differentiation of the lymphocyte-related gene IGHV3-23-like was associated with the H/L (heterophil/lymphocyte ratio) ratio. Overall, this study not only improved the capture ratio of regions with complex structures in novel sequences but also preliminarily explored the association of variations in these regions with chicken economic traits.

RDHCformer: Fusing ResDCN and Transformers for Fetal Head Circumference Automatic Measurement in 2D Ultrasound Images.

Fetal head circumference (HC) is an important biological parameter to monitor the healthy development of the fetus. Since there are some HC measurement errors that affected by the skill and experience of the sonographers, a rapid, accurate and automatic measurement for fetal HC in prenatal ultrasound is of great significance. We proposed a new one-stage network for rotating elliptic object detection based on anchor-free method, which is also an end-to-end network for fetal HC auto-measurement that no need for any post-processing. The network structure used simple transformer structure combined with convolutional neural network (CNN) for a lightweight design, meanwhile, made full use of powerful global feature extraction ability of transformer and local feature extraction ability of CNN to extract continuous and complete skull edge information. The two complement each other for promoting detection precision of fetal HC without significantly increasing the amount of computation. In order to reduce the large variation of intersection over union (IOU) in rotating elliptic object detection caused by slight angle deviation, we used soft stage-wise regression (SSR) strategy for angle regression and added KLD that is approximate to IOU loss into total loss function. The proposed method achieved good results on the HC18 dataset to prove its effectiveness. This study is expected to help less experienced sonographers, provide help for precision medicine, and relieve the shortage of sonographers for prenatal ultrasound in worldwide.

The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction.

Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in the whole brain, DRFs in local ischemic lesions, and their combination in predicting functional outcomes of ischemic stroke patients. First, the DRFs in the whole brain and the DRFs in local lesions of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) images are calculated. Second, the least absolute shrinkage and selection operator (Lasso) is used to generate four groups of DRFs, including the outstanding DRFs in the whole brain (Lasso (WB)), the outstanding DRFs in local lesions (Lasso (LL)), the combination of them (combined DRFs), and the outstanding DRFs in the combined DRFs (Lasso (combined)). Then, the performance of the four groups of DRFs is evaluated to predict the functional recovery in three months. As a result, Lasso (combined) in the four groups achieves the best AUC score of 0.971, which improves the score by 8.9% compared with Lasso (WB), and by 3.5% compared with Lasso (WB) and combined DRFs. In conclusion, the outstanding combined DRFs generated from the outstanding DRFs in the whole brain and local lesions can predict functional outcomes in ischemic stroke patients better than the single DRFs in the whole brain or local lesions.

Quantitative Distribution of Cerebral Venous Oxygen Saturation and Its Prognostic Value in Patients with Acute Ischemic Stroke.

This study investigated the quantitative distribution of cerebral venous oxygen saturation (SvO2) based on quantitative sensitivity mapping (QSM) and determined its prognostic value in patients with acute ischemic stroke (AIS). A retrospective study was conducted on 39 hospitalized patients. Reconstructed QSM was used to calculate the cerebral SvO2 of each region of interest (ROI) in the ischemic hemisphere. The intraclass correlation coefficient (ICC) and Bland−Altman analysis were conducted to define the best resolution of the distribution map. The correlation between the cerebral SvO2 in hypoxic regions (SvO2ROI < 0.7) and clinical scores was obtained by Spearman and power analysis. The associations between cerebral SvO2 and unfavorable prognosis were analyzed using multivariate logistic regression. Excellent agreement was found between the cerebral SvO2 in hypoxic regions with a resolution of 7.18 × 7.18 × 1.6 mm3 and asymmetrically prominent cortical veins regions (ICC: 0.879 (admission), ICC: 0.906 (discharge)). The cerebral SvO2 was significantly negative with clinical scores (all |r| > 0.3). The cerebral SvO2 and its changes at discharge were significantly associated with an unfavorable prognosis (OR: 0.812 and 0.866). Therefore, the cerebral SvO2 in hypoxic regions measured by the quantitative distribution map can be used as an indicator for evaluating the early prognosis of AIS.