Gene Function Expression Profile of Faba bean (Vicia faba) Seeds

Faba bean (Vicia faba L) is one of the important grain crops worldwide and its genome, the largest among grain legumes (approx. 13.4 Gb), has yet to be sequenced. Comprehensive knowledge of genes expressed in the crop's large seeds would not only help drive new gene c improvements in the crop but also aid its future genome characteriza on. Here, we applied high throughput RNA- Seq (Quan ca on) technology to compare gene expression pro les of seeds recovered from six faba bean varie es with divergent agronomic and seed quality a ributes. We iden ed a total of 47,621 Unigenes across all genotypes and a mean count of 38,712 per genotype, total genes length 27605508bp. Comparison between expression levels in lines possessing contras ng phenotypes allowed us to iden fy candidate genes that may be associated with key traits. In all pairwise comparisons of genotypes, pairwise up-regulated plus down-regulated di erences varied between 8,661 and 12,337 genes and co-expressed genes uctuated between 30,239 and 35,884. Overall, there was a mean of 24.2% genes that were di eren ally expressed between pairs of genotypes. They were similar of GO pro les generated between the two phenotypic traits (Hydra on Capacity and Pea seed-borne mosaic virus (PSbMV) pools and comparison of the GO pro les generated by all pairs of individual genotypes. This is the rst comprehensive analysis of gene expression gene c pro le on faba bean seeds.publishersversionPeer reviewe

A seismic prediction method of reservoir brittleness based on mineral composition and pore structure

The Lucaogou Formation, a typical fine-grained mixed formation in the Jimusaer Sag of the Junggar Basin, exhibits considerable potential for hydrocarbon exploration. Accurate brittle prediction is a crucial factor in determining hydraulic fracturing effectiveness. However, the area features complex lithological characteristics, including carbonate rocks, clastic rocks, volcanic rocks, and gypsum interbeds, along with thin layering and sporadic sweet spots. Traditional prediction methods offer limited resolution and there is an urgent need for a seismic brittle prediction method tailored to this complex geological environment. This paper presents a multi-mineral composition equivalent model for complex lithologies that enables the accurate calculation of Vp and Vs These ratios serve as the foundation for pre-stack elastic parameter predictions, which include Poisson’s ratio and Young’s modulus. By comparing the predicted parameters with well-logging measurements, the prediction accuracy is improved to 82%, with particularly high conformity in intervals characterized by high organic matter and clay content. Additionally, a three-dimensional brittle modeling approach reveals that the brittleness of the reservoir exceeds that of the surrounding rock, showing a gradual improvement in brittleness with increasing burial depth from southeast to northwest. The central area exhibits relatively good brittleness, with a stable, blocky distribution pattern

Automatic Root Cause Analysis via Large Language Models for Cloud Incidents

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years

Significant Near-Field Enhancement over Large Volumes around Metal Nanorods via Strong Coupling of Surface Lattice Resonances and Fabry–Pérot Resonance

Metal nanoparticles supporting plasmons are widely used to enhance electromagnetic fields, resulting in strong light–matter interactions at the nanoscale in a diverse range of applications. Recently, it has been shown that when metal nanorods are periodically arranged with proper lattice periods, surface lattice resonances (SLRs) can be excited and near fields can be greatly enhanced over extended volumes. In this work, we report significant near field enhancement over even larger volumes by placing the metal nanorod array within a Fabry–Pérot (F-P) microcavity. Simulation results show that by taking advantage of strong coupling between the SLR and the photonic F-P resonances, the electric field intensity of the bonding split mode can be enhanced by up to 1935 times, which is about three times of the enhancement of the SLR, and the greatly enhanced field can extend over most of the F-P microcavity. We further show that the F-P resonances of both odd and even orders can strongly couple to the SLR by varying the nanorods position from the middle of the microcavity. We expect that the proposed plasmonic-photonic coupling system will find promising applications in nanolasers, nonlinear optics and sensing

Identification of immune-related biomarkers and construction of regulatory network in patients with atherosclerosis

Abstract Background More and more evidence has established the crucial roles of the innate and adaptive immune systems in driving atherosclerosis-associated chronic inflammation in arterial blood vessels. Thus, the goal of this research was to determine immune-related biomarkers in atherosclerosis. Methods In this study, we conducted analysis on the mRNA expression profile of atherosclerosis obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) between atherosclerosis and control samples and immune-related genes (IRGs) were intersected to obtain differentially expressed immune-related genes (DEIRGs). The protein–protein interaction (PPI) network was created by STRING database and hub genes were identified by the MCODE plug-in. Furthermore, the receiver operating characteristic (ROC) curve was executed to verify the diagnostic value of the hub genes, and microRNA (miRNA)-gene-transcription factor (TF) regulatory networks were used to explain the regulatory mechanism of hub genes in atherosclerosis. Finally, qRT-PCR was performed to identify the mRNA levels of the target genes. Results A total of 199 overlapping genes were screened out as DEIRGs by intersecting the DEGs and IRGs. Then, 6 hub genes with high diagnostic value (IFIH1, IFIT1, IFIT2, IFIT3, ISG15 and OAS3) were identified via PPI network and ROC curve. Finally, miRNA-gene-TF networks revealed the regulatory mechanism of diagnostic genes.We used the carotid artery of AS patients and normal human carotid artery plaque samples for qRT-PCR verification, and the results showed that the hub gene had the same trend. Conclusion Our study identified IFIH1, IFIT1, IFIT2, IFIT3, ISG15 and OAS3 as immune-related hub genes of atherosclerosis. These genes may serve as potential therapeutic targets for atherosclerosis patients

Inverse Design of Broadband Dispersion Compensation Fiber Based on Deep Learning and Differential Evolution Algorithm

A Ge-doped dual-core dispersion compensation photonic crystal fiber (DC-DCPCF) is proposed. The small diameters of two layers’ air holes make DC-DCPCF form a dual-core structure, which is conducive to broadband dispersion compensation. Low Ge-doped silica as the only background material reduces the preparation difficulty and cost. It is inversely designed by using artificial neural network (ANN) combined with differential evolution algorithm (DE) to obtain target dispersion compensation. ANN replaces the finite element method to accomplish fast forward prediction of DC-DCPCF properties. DE solves the single solution problem of single or cascade network that makes it flexible and reproducible. The results demonstrate that the designed DC-DCPCF can not only compensate 45 and 25 times its length of Corning single-mode fiber 28 (SMF28) in S+C+L+U bands and E+S+C+L+U bands respectively, but also accurately compensate the residual dispersion with effective dispersion compensation being only +0.005∼+0.842ps/(nm·km) and −0.03∼+1.31ps/(nm·km), respectively. In addition, the kappa values of DCP-PCF are well matched with SMF28 in the broadband wavelength range. It takes only about 10 seconds to complete the inverse design of the target DC-DCPCF. It provides a design method for custom DC-DCPCF and an efficient inverse design solution for photonic automation in fiber optical communication systems

Error Investigation on Wi-Fi RTT in Commercial Consumer Devices

Researchers have explored multiple Wi-Fi features to estimate user locations in indoor environments in the past decade, such as Received Signal Strength Indication (RSSI), Channel State Information (CSI), Time of Arrival (TOA), and Angle of Arrive (AoA). Fine Time Measurement (FTM) is a protocol standardized by IEEE 802.11-2016, which can estimate the distance between the initiator and the station using Wi-Fi Round-Trip Time (RTT). Promoted by Google, such a protocol has been explored in many mobile localization algorithms, which can provide meter-level positioning accuracy between Wi-Fi RTT-enabled smartphones and access points (APs). However, previous studies have shown that the Wi-Fi RTT measurements are sensitive to environmental changes, which leads to significant errors in the localization algorithms. Such an error usually varies according to different environments and settings. Therefore, this paper investigates the error in Wi-Fi RTT distance measurements by setting multiple experiments with different hardware, motion status, and signal path loss conditions. The experiment results show that four categories of errors are found in RTT distance measurements, including hardware-dependent bias, blocker-dependent bias, fluctuations, and outliers. Comparison and analysis are carried out to illustrate the impact of the different errors on Wi-Fi RTT distance

Intraprocedural cerebral aneurysm rupture during endovascular coiling

Background : Intraprocedural aneurysm rupture is considered to be one of the most formidable complications of the endovascular treatment of cerebral aneurysms and is associated with high mortality. Objective : To report the clinical outcomes of cerebral aneurysms that ruptured during endovascular coiling. Patients and Methods : Over a period of six years, 559 endovascular embolizations were performed in 467 patients, with 507 cerebral aneurysms. Intraprocedural aneurysm rupture occurred in 14 cases (mean aneurysm size, 3.8 mm). Follow-up angiograms, at a minimum of three months post embolization, were available in 11 living patients. Acute and follow-up results were reviewed. Results : The difference in the rates of aneurysm perforation during endovascular coiling between ruptured and unruptured aneurysms was significant (P < 0.05). There were three (21.4%) deaths related to this complication and three (21.4%) patients developed new deficits (modified Rankin Scale scores 1 to 2). Acute results of embolization were: complete occlusion in eight (57.1%), neck remnant in two (14.3%), and incomplete occlusion in four (28.6%) patients. Long-term follow-up results in 11 living patients were: major recanalization in one (9.1%), minor recanalization in one (9.1%), and stable occlusion in nine (81.8%). Conclusion : Intraprocedural aneurysm rupture frequently occurs in small aneurysms and appears to be associated with relatively high rates of mortality