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Human retroviruses, including human T cell leukemia virus type 1 (HTLV-1) and HIV type 1 (HIV-1), encode an antisense gene in the negative strand of the provirus. Besides coding for proteins, the messenger RNAs (mRNAs) of retroviral antisense genes have also been found to regulate transcription directly. Thus, it has been proposed that retroviruses likely localize their antisense mRNAs to the nucleus in order to regulate nuclear events; however, this opposes the coding function of retroviral antisense mRNAs that requires a cytoplasmic localization for protein translation. Here, we provide direct evidence that retroviral antisense mRNAs are localized predominantly in the nuclei of infected cells. The retroviral 3′ LTR induces inefficient polyadenylation and nuclear retention of antisense mRNA. We further reveal that retroviral antisense RNAs retained in the nucleus associate with chromatin and have transcriptional regulatory function. While HTLV-1 antisense mRNA is recruited to the promoter of C-C chemokine receptor type 4 (CCR4) and enhances transcription from it to support the proliferation of HTLV-1–infected cells, HIV-1 antisense mRNA is recruited to the viral LTR and inhibits sense mRNA expression to maintain the latency of HIV-1 infection. In summary, retroviral antisense mRNAs are retained in nucleus, act like long noncoding RNAs instead of mRNAs, and contribute to viral persistence.

HTLV-1 is an oncovirus that encodes a transactivator Tax and a regulatory gene HBZ. HTLV-1 early or infectious replication depends on Tax; during HTLV-1 late infection, HBZ plays a crucial role in driving the proliferation of infected cells and maintaining viral persistence. The biphasic replication pattern of HTLV-1 dictated by Tax and HBZ represents a result of viral host adaptation, but how HTLV-1 coordinates Tax and HBZ expression to facilitate early and late infection remains elusive. Here we reveal that HBZ RNA splicing exhibits distinct patterns in Tax+ and Tax- HTLV-1 infected cells. We demonstrate that Tax interacts with the host spliceosome and inhibits HBZ splicing by competitively binding splicing factors including WDR83 and GPATCH1. As a result, Tax confers a natural constraint on HBZ, counterbalancing its anti-replication effect at HTLV-1 early infection, while unleashing HBZ to drive HTLV-1 mitotic propagation during late infection. The splicing-dependent restriction of HBZ by Tax thus represents a critical interplay central to HTLV-1 persistence.

In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators such as BTC Price and the VCRIX index. Our architecture’s analysis of emoji sentiment demonstrated a distinct advantage over FinBERT’s pure text sentiment analysis in such predicting power. This approach may be fed into the development of trading strategies aimed at utilizing social media elements to identify and forecast market trends. Crucially, our findings suggest that strategies based on emoji sentiment can facilitate the avoidance of significant market downturns and contribute to the stabilization of returns. This research underscores the practical benefits of integrating advanced AI-driven analyzes into financial strategies, offering a nuanced perspective on the interaction between digital communication and market dynamics in an academic context.

In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators like BTC Price and the VCRIX index. Our architecture's analysis of emoji sentiment demonstrated a distinct advantage over FinBERT's pure text sentiment analysis in such predicting power. This approach may be fed into the development of trading strategies aimed at utilizing social media elements to identify and forecast market trends. Crucially, our findings suggest that strategies based on emoji sentiment can facilitate the avoidance of significant market downturns and contribute to the stabilization of returns. This research underscores the practical benefits of integrating advanced AI-driven analyses into financial strategies, offering a nuanced perspective on the interplay between digital communication and market dynamics in an academic context.

Dual-active-bridge (DAB) is a DC/DC converter,which is commonly used in solid-state-transformer (SST) and electric vehicle (EV).In order to obtain the expected output voltage,the converter needs to be modeled and controlled.Firstly,the working modes in different time intervals of the switching cycle under single-phase-shift (SPS) modulation are analyzed,and the mathematical models of output voltage,current stress and phase-shifting duty cycle are constructed.Then,the simulation model is built on Simulink,and the PI controller is used for closed-loop voltage control,The accuracy of the mathematical model is verified.

Programmed death-ligand 1 (PD-L1) and T cell immunoreceptor with Ig and ITIM domains (TIGIT) are two potential targets for cancer immunotherapy, early clinical studies showed the combination therapy of anti-PD-L1 and anti-TIGIT had synergistic efficacy both in the terms of overall response rate (ORR) and overall survival (OS). It is rational to construct bispecific antibodies targeting PD-L1 and TIGIT, besides retaining the efficacy of the combination therapy, bispecific antibodies (BsAbs) can provide a new mechanism of action, such as bridging between tumor cells and T/NK cells. Here, we developed an IgG1-type bispecific antibody with optimal cytotoxicity. In this study, we thoroughly investigated 16 IgG-VHH formats with variable orientations and linker lengths, the results demonstrated that (G4S)2 linker not only properly separated two binding domains but also had the highest protein yield. Moreover, VHH-HC orientation perfectly maintained the binding and cytotoxicity activity of the variable domain of the heavy chain of heavy‐chain‐only antibody (VHH) and immunoglobulin G (IgG). Following treatment with BiPT-23, tumor growth was significantly suppressed in vivo, with more cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells infiltration, and selective depletion of Regulatory T cells (Tregs). BiPT-23 represents novel immunotherapy engineered to prevent hyperprogression of cancer with PD-1 blockade, and preferentially killed PD-L1 + tumor cells, and TIGIT + Tregs but maintained CD11b + F4/80 + immune cells within the tumor microenvironment (TME).

Calculating accurately iron bridge saturation effects of the magnetic field, for Permanent Magnet Assisted Synchronous Reluctance Motors (PMASynRMs), remains to be a knotty problem. This paper presents an analytical modeling method to predict open-circuit magnetic field distributions and electromagnetic performances of PMASynRMs, considering iron bridge saturation effects. This analytical modeling method combines the magnetic equivalent circuit method, superposition principle, the solution of the governing Maxwell’s field equations and a complex relative permeance function. A quadruple-layer PMASynRM are remodeled into four surface-inserted permanent magnet synchronous motors (SPMSMs) which have different surface-inserted permanent magnets. Each layer of the interior permanent magnets of the PMASynRM is transformed into a new equivalent surface-inserted permanent magnet whose equivalent thickness needs to be defined by the magnetic equivalent circuit method due to iron bridge saturation effects. Based on superposition principle and the solution of the Laplace’s or Poisson’s field equation, the distribution characteristics of the radial and tangential magnetic flux density are obtained for PMASynRMs. The slotting effect is considered by a complex relative permeance function. To confirm the accuracy of the proposed analytical modeling method, the analytical solutions of magnetic flux density and cogging torque have been compared with Finite Element Method (FEM) simulation results. The root mean square errors of radial and tangential magnetic flux density between analytical and FEM results are 0.000553 and 0.0000916 respectively. The results indicate that the proposed analytical modeling method can consider iron bridge saturation effects effectively, which is suitable for performance predictions and parametric studies of PMASynRMs.

To address the detection of stealthy False Data Injection Attacks (FDIA) that evade traditional detection mechanisms in smart grids, this paper proposes an unsupervised learning framework named SHAP-LUNAR (SHapley Additive ExPlanations-Learnable Unified Neighborhood-based Anomaly Ranking). This framework overcomes the limitations of existing methods, including parameter sensitivity, inefficiency in high-dimensional spaces, dependency on labeled data, and poor interpretability. Key contributions include (1) constructing a lightweight k-nearest neighbor graph through learnable graph aggregation to unify local anomaly detection, significantly reducing sensitivity to core parameters; (2) generating negative samples via boundary uniform sampling to eliminate dependency on real attack labels; (3) integrating SHAP for quantifying feature contributions to achieve feature-level model interpretation. Experimental results on IEEE 14-bus and IEEE 118-bus systems demonstrate F1 scores of 99.40% and 96.79%, respectively, outperforming state-of-the-art baselines. The method combines high precision, strong robustness, and interpretability.

Two-dimensional (2D) materials with remarkable second-harmonic generation (SHG) hold promise for future on-chip nonlinear optics. Relevant materials with both giant SHG response and environmental stability are long-sought targets. Here, we demonstrate the enormous SHG from the phase engineering of a high-performance semiconductor, Bi\\(_2\\)O\\(_2\\)Se (BOS), under uniaxial strain. SHG signals captured in strained 20 nm-BOS films exceed those of NbOI\\(_2\\) and NbOCl\\(_2\\) of similar thickness by a factor of 10, and are four orders of magnitude higher than monolayer-MoS\\(_2\\), resulting in a significant second-order nonlinear susceptibility on the order of 1 nm V\\(^{-1}\\). Intriguingly, the strain enables continuous adjustment of the ferroelectric phase transition across room temperature. Consequently, an exceptionally large tunability of SHG, approximately six orders of magnitude, is achieved through strain or thermal modulation. This colossal SHG, originating from the geometric phase of Bloch wave functions and coupled with sensitive tunability through multiple approaches in this air-stable 2D semiconductor, opens new possibilities for designing chip-scale, switchable nonlinear optical devices.