Detecting extreme-mass-ratio inspirals for space-borne detectors with deep learning

One of the primary objectives for space-borne gravitational wave detectors is the detection of extreme-mass-ratio inspirals (EMRIs). This undertaking poses a substantial challenge because of the complex and long EMRI signals, further complicated by their inherently faint signal. In this research, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors. Our method employs the Q-transform for data preprocessing, effectively preserving EMRI signal characteristics while minimizing data size. By harnessing the robust capabilities of CNNs, we can reliably distinguish EMRI signals from noise, particularly when the signal-to-noise~(SNR) ratio reaches 50, a benchmark considered a ``golden'' EMRI. At the meantime, we incorporate time-delay interferometry (TDI) to ensure practical utility. We assess our model's performance using a 0.5-year dataset, achieving a true positive rate~(TPR) of 94.2\% at a 1\% false positive rate~(FPR) across various signal-to-noise ratio form 50-100, with 91\% TPR and 1\% FPR at an SNR of 50. This study underscores the promise of incorporating deep learning methods to advance EMRI data analysis, potentially leading to rapid EMRI signal detection.Comment: 12 pages, 8 figures, 2 table

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by their inherently faint nature. In this letter, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9 % at a 1 % false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as mass and spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can be inferred directly by employing a VGG network. The mass and spin of the SMBH can be determined at 99 % and 92 % respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation .Comment: 6 pages, 5 figure

Power Control for Coordinated NOMA Downlink with Cell-Edge Users

International audienceNon-orthogonal multiple access (NOMA) is an effective means to improve the spectral efficiency of a wireless communication system. When applied to cellular networks, cell edge users may suffer from low bit rate, or the associated base stations may need to use excessively high power to serve those users. In order to alleviate the problem, this paper considers the integration of NOMA with coordinated transmission techniques. A two-cell system is considered, in which there are two users near their associated base stations and a cell edge user served by both base stations. It is assumed that each user has a data rate requirement , and the system objective is to minimize the total transmit power. With a formal problem formulation, the feasibility of the problem is characterized by using Helly's theorem. When the problem is feasible, we design both centralized and distributed algorithms to solve it. Numerical results show that NOMA can significantly outperform an orthogonal multiple access scheme in terms of power consumption and outage probability. Index Terms: Non-orthogonal multiple access (NOMA), coordinated multipoint (CoMP), power control, power domain multiplexing

Bovine lactoferricin exerts antibacterial activity against four Gram-negative pathogenic bacteria by transforming its molecular structure

The emergence and development of pathogenic bacterial resistance to antibiotics pose significant challenges to human health. Antimicrobial peptides (AMPs) are considered promising alternatives to conventional antibiotics. Lactoferricin (Lfcin), a cationic AMP located in the N-terminal region of lactoferrin, serves as the antimicrobial active center of the intact protein. The presence of two cysteines in Lfcin allows for the formation of an intramolecular disulfide bond, which may influence its molecular structure and antibacterial function. To investigate this hypothesis, we synthesized, purified, and identified bovine Lfcin along with two derivatives: Lfcin with a disulfide bond (Lfcin DB) and a mutated form that cannot form the disulfide bond (Lfcin C36G). We analyzed the circular dichroism spectra of these peptides under varying ionic and hydrophobic conditions, while their tertiary structures were predicted using AlphaFold3. Results indicated that increased ionic strength reduced the random coil ratios across all peptides. The secondary structure of Lfcin showed similar percentages with Lfcin C36G in the H2O and similar ratios with Lfcin DB under hydrophobic conditions. AlphaFold3-predicted models revealed two distinct structures: one predominantly adopting α-helix conformations and the other characterized by β-sheet topology. Furthermore, we evaluated the antibacterial activity of the peptides against four Gram-negative bacteria, including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Salmonella gallinarum. The synthetic peptides demonstrated broad-spectrum antibacterial activity, with Lfcin exhibiting superior efficacy compared to its derivatives. Our findings suggest that Lfcin can reversibly interconvert between two distinct molecular states under varying ionic strengths and hydrophobic effects, with the resulting structural transformations enhancing its antibacterial function

Oxygen Vacancy-Enriched Amorphous Transition Metal Ternary Oxides toward Highly Efficient Oxygen Evolution Reaction

Developing highly efficient oxygen evolution reaction (OER) electrocatalysts based on earth-abundant elements is critical to improve the efficiency of water electrolysis, but it remains a challenge. Herein, an amorphous ternary oxides composites FeNiCoOx/CoOx with rich oxygen vacancies are developed through a low-cost wet chemical deposition strategy toward this challenge. Benefiting from the synergistic effect of multimetal atom interaction and high exposure of active sites caused by oxygen vacancies and amorphous structure, the as-developed FeNiCoOx/CoOx electrocatalyst exhibits an exceptional catalytic performance with a low overpotential of only 221 mV at a current density of 100 mA cm-2 and negligible performance degradation over 240 h. Furthermore, the FeNiCoOx/CoOx-assembled anion exchange membrane water electrolyzer (AEMWE) can achieve a high current density of 1 A cm-2 at a low voltage of 1.765 V, demonstrating its great potential for practical application

Amorphous quaternary alloy nanoplates for efficient catalysis of hydrogen evolution reaction

Developing a highly efficient non-precious transition metal-based electrocatalyst via a facile approach toward the hydrogen evolution reaction (HER) is critical for large-scale hydrogen production but still remains challenging. Herein, a cost-effective electrochemical deposition strategy is rationally proposed to construct amorphous quaternary FeCoNiCu alloy nanosheets supported on nickel foam (NF) towards this challenge. Benefiting from the synergistic effect of multi-metal atoms interaction and the high exposure of active sites caused by abundant open voids, the as-synthesized FeCoNiCu/NF electrode exhibits high catalytic activity and robustness toward HER in alkaline solution, requiring an overpotential of only 35 mV to reach a current density of 10 mA cm–2. This study may pave a new avenue to design advanced electrocatalyst for energy conversion

YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features

The high-throughput and full-time acquisition of images of crop growth processes, and the analysis of the morphological parameters of their features, is the foundation for achieving fast breeding technology, thereby accelerating the exploration of germplasm resources and variety selection by crop breeders. The evolution of embryonic soybean radicle characteristics during germination is an important indicator of soybean seed vitality, which directly affects the subsequent growth process and yield of soybeans. In order to address the time-consuming and labor-intensive manual measurement of embryonic radicle characteristics, as well as the issue of large errors, this paper utilizes continuous time-series crop growth vitality monitoring system to collect full-time sequence images of soybean germination. By introducing the attention mechanism SegNext_Attention, improving the Segment module, and adding the CAL module, a YOLOv8-segANDcal model for the segmentation and extraction of soybean embryonic radicle features and radicle length calculation was constructed. Compared to the YOLOv8-seg model, the model respectively improved the detection and segmentation of embryonic radicles by 2% and 1% in mAP50-95, and calculated the contour features and radicle length of the embryonic radicles, obtaining the morphological evolution of the embryonic radicle contour features over germination time. This model provides a rapid and accurate method for crop breeders and agronomists to select crop varieties

N7-Methylation of the Coronavirus RNA Cap Is Required for Maximal Virulence by Preventing Innate Immune Recognition

The ongoing coronavirus (CoV) disease 2019 (COVID-19) pandemic caused by infection with severe acute respiratory syndrome CoV 2 (SARS-CoV-2) is associated with substantial morbidity and mortality. Understanding the immunological and pathological processes of coronavirus diseases is crucial for the rational design of effective vaccines and therapies for COVID-19. Previous studies showed that 2′-O-methylation of the viral RNA cap structure is required to prevent the recognition of viral RNAs by intracellular innate sensors. Here, we demonstrate that the guanine N7-methylation of the 5′ cap mediated by coronavirus nonstructural protein 14 (nsp14) contributes to viral evasion of the type I interferon (IFN-I)-mediated immune response and pathogenesis in mice. A Y414A substitution in nsp14 of the coronavirus mouse hepatitis virus (MHV) significantly decreased N7-methyltransferase activity and reduced guanine N7-methylation of the 5′ cap in vitro. Infection of myeloid cells with recombinant MHV harboring the nsp14-Y414A mutation (rMHVnsp14-Y414A) resulted in upregulated expression of IFN-I and ISG15 mainly via MDA5 signaling and in reduced viral replication compared to that of wild-type rMHV. rMHVnsp14-Y414A replicated to lower titers in livers and brains and exhibited an attenuated phenotype in mice. This attenuated phenotype was IFN-I dependent because the virulence of the rMHVnsp14-Y414A mutant was restored in Ifnar−/− mice. We further found that the comparable mutation (Y420A) in SARS-CoV-2 nsp14 (rSARS-CoV-2nsp14-Y420A) also significantly decreased N7-methyltransferase activity in vitro, and the mutant virus was attenuated in K18-human ACE2 transgenic mice. Moreover, infection with rSARS-CoV-2nsp14-Y420A conferred complete protection against subsequent and otherwise lethal SARS-CoV-2 infection in mice, indicating the vaccine potential of this mutant. IMPORTANCE Coronaviruses (CoVs), including SARS-CoV-2, the cause of COVID-19, use several strategies to evade the host innate immune responses. While the cap structure of RNA, including CoV RNA, is important for translation, previous studies indicate that the cap also contributes to viral evasion from the host immune response. In this study, we demonstrate that the N7-methylated cap structure of CoV RNA is pivotal for virus immunoevasion. Using recombinant MHV and SARS-CoV-2 encoding an inactive N7-methyltransferase, we demonstrate that these mutant viruses are highly attenuated in vivo and that attenuation is apparent at very early times after infection. Virulence is restored in mice lacking interferon signaling. Further, we show that infection with virus defective in N7-methylation protects mice from lethal SARS-CoV-2, suggesting that the N7-methylase might be a useful target in drug and vaccine development

Aloperine Suppresses Cancer Progression by Interacting with VPS4A to Inhibit Autophagosome-lysosome Fusion in NSCLC.

Aloperine (ALO), a quinolizidine-type alkaloid isolated from a natural Chinese herb, has shown promising antitumor effects. Nevertheless, its common mechanism of action and specific target remain elusive. Here, it is demonstrated that ALO inhibits the proliferation and migration of non-small cell lung cancer cell lines in vitro and the tumor development in several mouse tumor models in vivo. Mechanistically, ALO inhibits the fusion of autophagosomes with lysosomes and the autophagic flux, leading to the accumulation of sequestosome-1 (SQSTM1) and production of reactive oxygen species (ROS), thereby inducing tumor cell apoptosis and preventing tumor growth. Knockdown of SQSTM1 in cells inhibits ROS production and reverses ALO-induced cell apoptosis. Furthermore, VPS4A is identified as a direct target of ALO, and the amino acids F153 and D263 of VPS4A are confirmed as the binding sites for ALO. Knockout of VPS4A in H1299 cells demonstrates a similar biological effect as ALO treatment. Additionally, ALO enhances the efficacy of the anti-PD-L1/TGF-β bispecific antibody in inhibiting LLC-derived subcutaneous tumor models. Thus, ALO is first identified as a novel late-stage autophagy inhibitor that triggers tumor cell death by targeting VPS4A