BackgroundDuring mammalian pre-implantation embryonic development (PED), the process of maternal-to-zygote transition (MZT) is well orchestrated by epigenetic modification and gene sequential expression, and it is related to the embryonic genome activation (EGA). During MZT, the embryos are sensitive to the environment and easy to arrest at this stage in vitro. However, the timing and regulation mechanism of EGA in buffaloes remain obscure. ResultsBuffalo pre-implantation embryos were subjected to trace cell based RNA-seq and whole-genome bisulfite sequencing (WGBS) to draw landscapes of transcription and DNA-methylation. Four typical developmental steps were classified during buffalo PED. Buffalo major EGA was identified at the 16-cell stage by the comprehensive analysis of gene expression and DNA methylation dynamics. By weighted gene co-expression network analysis, stage-specific modules were identified during buffalo maternal-to-zygotic transition, and key signaling pathways and biological process events were further revealed. Programmed and continuous activation of these pathways was necessary for success of buffalo EGA. In addition, the hub gene, CDK1, was identified to play a critical role in buffalo EGA.ConclusionsOur study provides a landscape of transcription and DNA methylation in buffalo PED and reveals deeply the molecular mechanism of the buffalo EGA and genetic programming during buffalo MZT. It will lay a foundation for improving the in vitro development of buffalo embryos.Graphical Abstract

Optimizing the quantum circuit for implementing Advanced Encryption Standard (AES) is crucial for estimating the necessary resources in attacking AES by the Grover algorithm. Previous studies have reduced the number of qubits required for the quantum circuits of AES-128/-192/-256 from 984/1112/1336 to 270/334/398, which is close to the optimal value of 256/320/384. It becomes a challenging task to further optimize them. AimTaking aim at this task, we find a method for how the quantum circuit of AES S-box can be designed with the help of the automation tool LIGHTER-R. Particularly, the multiplicative inversion in F28, which is the main part of the S-box, is converted into the multiplicative inversion (and multiplication) in F24, then the latter can be implemented by LIGHTER-R because its search space is small enough. By this method, we construct the quantum circuits of S-box for mapping |a⟩|0⟩ to |a⟩|S(a)⟩ and |a⟩|b⟩ to |a⟩|b ⊕ S(a)⟩ with 20 qubits instead of 22 in the previous studies. In addition, we introduce new techniques to reduce the number of qubits required by the S-box circuit for mapping |a⟩ to |S(a)⟩ from 22 in the previous studies to 16. Accordingly, we synthesize the quantum circuits of AES-128/-192/-256 with 264/328/392 qubits, which implies a new record.

ObjectiveTo identify the correlation between finger-to-floor distance(FFD) and the spinal function indices and disease activity scores of ankylosing spondylitis (AS) via a multicentre case–control study, and to calculate the optimal cutoff value of FFD using statistical methods.MethodsPatients with AS and healthy individuals were recruited, and the FFD and other spinal mobility values were measured. The correlation between the FFD and the Bath Ankylosing Spondylitis Metric Index (BASMI), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI) was analyzed using Spearman rank correlation analysis. Receiver operating characteristic (ROC) curves of FFD stratified by gender and age were drawn and their optimal cutoff values were determined.ResultsA total of 246 patients with AS and 246 healthy subjects were recruited. The FFD was strongly correlated with BASMI (r = 0.72, p < 0.001), moderately correlated with BASFI (r = 0.50, p < 0.001) and weakly correlated with BASDAI (r = 0.36, p < 0.001). The lowest cutoff value of the FFD was 2.6 cm while the highest was 18.4 cm. Moreover, the FFD was significantly correlated with sex and age.ConclusionThere exists a strong correlation between the FFD and spinal mobility, a moderately correlation and function, which provides reliable data for the evaluation of patients with AS in clinical settings and the rapid screening of low back pain-related diseases in the general population. Furthermore, these findings have clinical potential in improving the missed diagnosis or delayed diagnosis of low back pain.

Adenovirus pneumonia is common in pediatric upper respiratory tract infection, which is comparatively easy to develop into severe cases and has a high mortality rate with many influential sequelae. As for pathogenesis, adenoviruses can directly damage target cells and activate the immune response to varying degrees. Early clinical recognition depends on patients’ symptoms and laboratory tests, including those under 2 years old, dyspnea with systemic toxic symptoms, atelectasis or emphysema in CT image, decreased leukocytes, and significantly increased C-reaction protein (CRP) and procalcitonin (PCT), indicating the possibility of severe cases. Until now, there is no specific drug for adenovirus pneumonia, so in clinical practice, current treatment comprises antiviral drugs, respiratory support and bronchoscopy, immunomodulatory therapy, and blood purification. Additionally, post-infectious bronchiolitis obliterans (PIBO), hemophagocytic syndrome, and death should be carefully noted. Independent risk factors associated with the development of PIBO are invasive mechanical ventilation, intravenous steroid use, duration of fever, and male gender. Meanwhile, hypoxemia, hypercapnia, invasive mechanical ventilation, and low serum albumin levels are related to death. Among these, viral load and serological identification are not only “gold standard” for adenovirus pneumonia, but are also related to the severity and prognosis. Here, we discuss the progress of pathogenesis, early recognition, therapy, and risk factors for poor outcomes regarding severe pediatric adenovirus pneumonia.

Brain–computer interfaces (BCIs) have garnered extensive interest and become a groundbreaking technology to restore movement, tactile sense, and communication in patients. Prior to their use in human subjects, clinical BCIs require rigorous validation and verification (V&V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&V, due to their proximity to humans. This literature review summarizes 94 NHP gait analysis studies until 1 June, 2022, including seven BCI-oriented studies. Due to technological limitations, most of these studies used wired neural recordings to access electrophysiological data. However, wireless neural recording systems for NHPs enabled neuroscience research in humans, and many on NHP locomotion, while posing numerous technical challenges, such as signal quality, data throughout, working distance, size, and power constraint, that have yet to be overcome. Besides neurological data, motion capture (MoCap) systems are usually required in BCI and gait studies to capture locomotion kinematics. However, current studies have exclusively relied on image processing-based MoCap systems, which have insufficient accuracy (error: ≥4° and 9 mm). While the role of the motor cortex during locomotion is still unclear and worth further exploration, future BCI and gait studies require simultaneous, high-speed, accurate neurophysiological, and movement measures. Therefore, the infrared MoCap system which has high accuracy and speed, together with a high spatiotemporal resolution neural recording system, may expand the scope and improve the quality of the motor and neurophysiological analysis in NHPs.

Background: Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the treatment strategy selection and prognosis evaluation of glioma. The purpose of this study is to predict the IDH mutation status of gliomas based on multicenter magnetic resonance (MR) images using radiomic models, which were composed from the selected radiomics features and logistic regression (LR), support vector machine (SVM), and LR least absolute shrinkage and selection operator (LASSO) classifiers. Methods: We retrospectively reviewed the medical records of 205 patients with gliomas. We enrolled 78 patients from Shandong Provincial Hospital from January 2018 to December 2019 as testing sets and 127 patients from The Cancer Genome Atlas (TCGA) as training sets. Preoperative MR images were stratified according to their IDH status, and the participants formed a consecutive and random series. Four MR modalities, including T1C, T2, T1 fluid-attenuated inversion recovery (FLAIR), and T2 FLAIR, were used for analysis. Five-fold cross-validation was adopted to train the models, and the models’ performances were verified through the testing set. Tumor volumes of interest (VOI) were delineated on the 4 MR modalities. A total of 428 radiomics features were extracted. Two feature selection algorithms, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), were used to select radiomics features. These features were fed into 3 machine learning classifiers, which were LR, SVM, and LR LASSO, to construct prediction models. The accuracy (ACC), sensitivity (SEN), specificity (SPEC), and area under the curve (AUC) were applied to measure the predictive performance of the radiomics models. Results: The LR (SVM and LR LASSO) classifier predicted IDH mutation status with an average testing set ACC of 80.77% (80.64% and 80.41%), a SEN of 73.68% (84.21% and 89.47%), a SPEC of 87.50% (67.50% and 62.50%), and an AUC of 0.8572 (0.8217 and 0.8164). Conclusions: The radiomics models based on MR modalities demonstrated the potential to be used as tools across different data sets for the noninvasive prediction of the IDH mutation status in glioma.