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Abstract The dwarf surf clam, Mulinia lateralis, is considered as a model species for bivalves because of its rapid growth and short generation time. Recently, successful breeding of this species for multiple generations in our laboratory revealed its acquisition of adaptive advantages during artificial breeding. In this study, 310 individuals from five different generations were genotyped with 22,196 single nucleotide polymorphisms (SNPs) with the aim of uncovering the genetic basis of their adaptation to laboratory conditions. Results revealed that M. lateralis consistently maintained high genetic diversity across generations, characterized by high observed heterozygosity (Ho: 0.2733–0.2934) and low levels of inbreeding (Fis: −0.0244–0.0261). Population analysis indicated low levels of genetic differentiation among generations of M. lateralis during artificial breeding (Fst

Objective. To investigate the value of preoperative urinary nuclear matrix protein 22 (NMP22) and Cystatin B (CSTB) expressions in evaluating the postoperative recurrence of bladder cancer. Methods. The clinical case data of 102 patients with bladder cancer who underwent surgical treatment from January 2017 to January 2022 were collected, and the patients were divided into a recurrence group (n=54) and nonrecurrence group (n=48) according to whether the patients recurred after surgery, and the preoperative NMP22 and CSTB expression levels between the two groups were compared. Receiver operating curve (ROC) was used to analyze the evaluation value of preoperative NMP22 and CSTB expression in patients with bladder cancer postoperative recurrence. Logistic multivariate regression method was used to analyze the correlation between preoperative NMP22 and CSTB expression and postoperative bladder cancer recurrence. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of NMP22 and CSTB single detection and combined detection were evaluated for postoperative recurrence of bladder cancer. Results. The preoperative expression levels of NMP22 and CSTB in the recurrence group were significantly higher than those in the nonrecurrence group (P

Sufficient synthetic aperture radar (SAR) data is the key element in achieving excellent target recognition performance for most deep learning algorithms. It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has become an effective way to obtain sufficient samples. Simulated and measured SAR images are nonhomologous data. Due to the fact that the target geometric model of SAR simulation is not inevitably consistent with the real object, the SAR sensor model in SAR simulation may be different from the actual sensor, the background environment of the object is also inevitably different from that of SAR simulation, the error of electromagnetic modeling method itself, and so on. There are inevitable differences between the simulated and measured SAR images, which will affect the recognition performance. To address this problem, an SAR simulation method based on a high-frequency asymptotic technique and a discrete ray tracing technique is proposed in this paper to obtain SAR simulation images of ground vehicle targets. Next, various convolutional neural networks (CNNs) and AugMix data augmentation methods are proposed to train only on simulated data, and then target recognition on MSTAR measured data is performed. The experiments show that all the CNNs can achieve incredible recognition performance on the nonhomologous SAR data, and the RegNetX-3.2GF achieves state-of-the-art performance, up to 84.81%.

Compared to traditional transformer, power electronic transformer (PET) has a better control ability as it can manage the terminal power independently. This makes it very promising for modern power distribution systems with high penetration of renewable energy. However, the unique structure of PET increases the coupling between AC and DC network, which enhances the complexity of the traditional alternating iterative method in an AC–DC network. Since the structure of PET is based on power electronics, the power losses will influence the OPF results and have to be taken into account. This paper proposes a novel extension branch model with an accurate PET power loss model that can solve the AC–DC power flow problem in a unified way. In this method, a branch model of PET with power loss is first established. The AC and DC branches are then combined to generate an extended branch model of the whole AC–DC network. An AC–DC hybrid test system connected by two 3-port PET is used to verify the effectiveness of the proposed model via simulations. The results show that this method has a superior convergence speed than the alternating iterative model.

Abstract Objective Renal function can modify the outcomes of large vessel occlusion (LVO) stroke across stroke etiologies in disparate degrees. The presence of renal function deficit can also impair the pharmacokinetics of tirofiban. Hence, this study aimed to investigate the roles of renal function in determining efficacy and safety of intravenous tirofiban before endovascular treatment (EVT) for acute ischemic stroke patients with large vessel occlusion (LVO). Methods This study was a post hoc exploratory analysis of the RESCUE‐BT trial. The primary outcome was the proportion of patients achieving functional independence (modified Rankin scale 0–2) at 90 days, and the primary safety outcome was the rate of symptomatic intracranial hemorrhage (sICH). Results Among 908 individuals with available serum creatinine, decreased estimated glomerular filtration rate (eGFR) status was noted more commonly in patients with cardioembolic stroke (CE), while large artery atherosclerosis (LAA) was predominant in patients with normal renal function. In LAA with normal renal function, tirofiban was associated with higher rates of functional independence at 90 days (41.67% vs 59.80%, p = 0.003). However, for LVO patients with renal dysfunction, tirofiban did not improve functional outcomes for any of the etiologies (LAA, p = 0.876; CE, p = 0.662; others, p = 0.894) and significantly increased the risk of sICH among non‐LAA patients (p = 0.020). Mediation analysis showed tirofiban reduced thrombectomy passes (12.27%) and drug/placebo to recanalization time (14.25%) mediated its effects on functional independence. Conclusion This present study demonstrated the importance of evaluating renal function before administering intravenous tirofiban among patients with LVO who are planned to undergo EVT.

Abstract Background Evidence from observational studies and clinical trials suggests an association between periodontal disease and Alzheimer's disease (AD). However, the causal relationship between periodontal disease and AD remains to be determined. Methods We obtained periodontal disease data from the FinnGen database and two sets of AD data from the IEU consortium and PGC databases. Subsequently, we conducted a two‐sample Mendelian randomization (MR) analysis to investigate the causal relationship between periodontal disease and AD. Results The results of the random‐effects IVW analysis revealed no evidence of a genetic causal relationship between periodontal disease and AD, regardless of whether the AD data from the IEU consortium or the AD data from the PGC database were utilized. No heterogeneity, multiple effects of levels, or outliers were observed in this study. Conclusions Our findings indicate that there is no causal relationship between periodontal disease and AD at the genetic level.

Abstract Developing plasmonic biosensors that are low‐cost, portable, and relatively simple to operate remains challenging. Herein, a novel metasurface plasmon‐etch immunosensor is described, namely a nanozyme‐linked immunosorbent surface plasmon resonance biosensor, for the ultrasensitive and specific detection of cancer biomarkers. Gold‐silver composite nano cup array metasurface plasmon resonance chip and artificial nanozyme‐labeled antibody are used in two‐way sandwich analyte detection. Changes in the biosensor's absorption spectrum are measured before and after chip surface etching, which can be applied to immunoassays without requiring separation or amplification. The device achieved a limit of alpha‐fetoprotein (AFP) detection < 21.74 fM, three orders of magnitude lower than that of commercial enzyme‐linked immunosorbent assay kits. Additionally, carcinoembryonic antigen (CEA) and carbohydrate antigen 125 (CA125) are used for quantitative detection to verify the universality of the platform. More importantly, the accuracy of the platform is verified using 60 clinical samples; compared with the hospital results, the three biomarkers achieve high sensitivity (CEA: 95.7%; CA125: 90.9%; AFP: 86.7%) and specificity (CEA: 97.3%; CA125: 93.9%; AFP: 97.8%). Due to its rapidity, ease of use, and high throughput, the platform has the potential for high‐throughput rapid detection to facilitate cancer screening or early diagnostic testing in biosensing.

Abstract Syringin is a natural chemical compound first isolated from the bark of lilac and is known to have neuroprotective effects in middle cerebral artery occlusion (MCAO). Volume regulated anion channel (VRAC) is a cell swelling‐activated anion channel, which is implicated in brain ischemia. However, the mechanism underlying the syringin protecting the neuron from damage in MCAO is still unclear. We hypothesized that syringin has an inhibitory effect on the opening of VRAC channels. To access the effect of syringin on VRAC currents and predict how syringin interacts with VRAC proteins, we performed whole‐cell patch‐clamp experiments using HEK293 cells. Initially, HEK293 cells were perfused with isotonic extracellular solution, followed by hypotonic extracellular solution to stimulate endogenous VRAC currents. Once the VRAC currents reached a steady state, the hypotonic solution containing syringin was perfused to study the effect of syringin on VRAC currents. The potential interaction between syringin and the VRAC protein was investigated using molecular docking as a predictive model. In this study, we found that syringin moderately inhibited VRAC currents in a dose‐dependent manner. The potential binding of syringin to LRRC8 protein was predicted through in silico molecular docking, which suggests an affinity of −6.6 kcal/mol and potential binding sites of arginine 103 and leucine 101. Our results herein characterize syringin as an inhibitor of the VRAC channels, which provides valuable insights for the future development of VRAC channel inhibitors.

Abstract Preeclampsia (PE) is a multisystem disorder with high maternal morbidity and mortality rates. Currently, no practical therapeutic approach is available to prevent PE progression, except for early delivery. Gut dysbiosis is associated with PE development. Previous data showed that the abundance of Akkermansia muciniphila (Am) was lower in patients with PE than in normotensive pregnant women. Here, in this study, decreased abundance of Am was observed in a PE mouse model. Also, we found that administration with Am could significantly attenuate systolic blood pressure, promote foetal growth and improve the placental pathology in mice with PE. Moreover, Am‐derived extracellular vesicles (AmEVs) were transferred from the gastrointestinal (GI) tract to the placenta and mitigated pre‐eclamptic symptoms in PE mice. These beneficial effects of AmEVs were mediated by enhanced trophoblast invasion of the spiral artery (SpA) and SpA remodelling through activation of the epidermal growth factor receptor (EGFR)–phosphatidylinositol‐3‐kinase (PI3K)–protein kinase B (AKT) signalling pathway. Collectively, our findings revealed the potential benefit of using AmEVs for PE treatment and highlighted important host–microbiota interactions.

Abstract Long noncoding RNAs (lncRNAs) dysregulation leads to malignant progression of lung cancer. Our study profiled differentially expressed lncRNA and mRNA in tumor and normal tissues of lung adenocarcinoma (LUAD). Further, analysis of the gene expression profiles of LUAD tissues (n = 533) and normal tissues (n = 59) from The Cancer Genome Atlas (TCGA). A total of 138 lncRNAs were differentially expressed between LUAD tissues and normal tissues (false discovery rate [FDR] q

Coherent anti-Stokes Raman spectroscopy (CARS) and Ccherent anti-Stokes hyper-Raman spectroscopy (CAHRS), as other high-order nonlinear spectroscopy techniques, are widely exploited in many research fields, such as dynamic processes, gene expression spectrum screening, high-resolution spectroscopy, and nonlinear high-resolution imaging. However, it is difficult to make a quantitative analysis of the spectral signals that involve a large number of high-order micropolarizability tensors. It is reported that the CARS and CAHRS microscopic hyperpolarizability tensor elements can be decomposed into the product of the differentiation of Raman microscopic polarizability tensor α′i′j′ and hyper-Raman microscopic polarizability tensor β′i′j′k′ so that the high-order spectra can be simplified to the analysis of low-order spectra. In this paper, we use the bond additivity model (BAM) combined with experimental corrections to address the carbon dioxide (CO2) molecule and present the simplified scheme for differentiation of hyper-Raman microscopic polarizability tensor elements β′i′j′k′. Taking advantage of this approach, combined with the experimental correction, the differentiation of Hyper-Raman microscopic polarizability tensor elements β′i′j′k′ of the CO2 is obtained and the expressions of β′i′j′k′ for antisymmetric vibrations of CO2 are deduced. Finally, substituting the differentiation of Raman microscopic polarizability tensor elements α′i′j′ reported in the literature into the ratio above can obtain the proportional relationship between the microscopic polarizability tensor elements of CARS and CAHRS of the CO2. This method can provide the basis for the quantitative analysis of high-order nonlinear spectral profiles.

Middle cerebral artery aneurysm is a common type of intracranial aneurysm in neurosurgery, accounting for about 20% of intracranial aneurysms, and is the third most common site of intracranial aneurysms. The surgical success rate and postoperative recovery ability of today’s treatment plans are not satisfactory. Therefore, this paper designs a health model based on data analysis to clinically apply clipping surgery for cerebral aneurysm. This paper studies data analytics health models in the context of big data analytics. The model combines the characteristics of cerebral aneurysms for targeted analysis, and then through the understanding of the clipping treatment of cerebral aneurysms, this paper combines the deep learning in the neural network to train the treatment plan under the data analysis health model. Finally, this paper designs a therapeutic plan for clipping treatment of cerebral aneurysm based on a data analysis health model. To verify its data analysis ability, this paper designs experiments on unbalanced data sets and experiments to improve the execution efficiency of the algorithm. After analyzing the results obtained from the experiment, this paper will apply them to the clinic. The final experiment showed that the surgical success rate of the clipping treatment for cerebral aneurysm based on the data analysis health model was increased by 21.84% compared with the traditional clipping treatment for cerebral aneurysm.

Mechanical properties of intact completely decomposed granite (CDG) soil, widely distributed in South China, decrease drastically as encountering external load-related disturbance or soaked by water. In this study, staged triaxial consolidated drained (CD) test and microscopic scanning were conducted using intact CDG specimens extracted from a construction site to investigate the mechanical behavior and microscopic pore distribution. The results show that the stress-strain relationship reveals a shrinking behavior in the first-stage loading and a brittle behavior in the second-stage loading. The development of cracks is affected by the principal stress, which causes the pores and cracks to shrink or partially close. In addition, Esec-1 increases linearly with the increase of confining pressure, but Esec-2 decreases exponentially.

Abstract Noninvasive prenatal detection of monogenic diseases based on cell‐free DNA is hampered by challenges in obtaining a sufficient fraction and adequate quality of fetal DNA. Analyzing rare trophoblastic cells from Papanicolaou smears carrying the entire fetal genome provides an alternative method for noninvasive detection of monogenic diseases. However, intracellular labeling for identification of target cells can affect the quality of DNA in varying degrees. Here, a new approach is developed for nondestructive identification of rare fetal cells from abundant maternal cells based on endoplasmic reticulum staining and linear discriminant analysis (ER‐LDA). Compared with traditional methods, ER‐LDA has little effect on cell quality, allowing trophoblastic cells to be analyzed on the single‐cell level. Using ER‐LDA, high‐purity of trophoblastic cells can be identified and isolated at single cell resolution from 60 pregnancies between 4 and 38 weeks of gestation. Pathogenic variants, including –SEA/ deletion mutation and point mutations, in 11 fetuses at risk for α‐ or β‐thalassemia can be accurately detected by this test. The detection platform can also be extended to analyze the mutational profiles of other monogenic diseases. This simple, low‐cost, and noninvasive test can provide valuable fetal cells for fetal genotyping and holds promise for prenatal detection of monogenic diseases.