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To learn how genomic sequence influences multiscale three-dimensional (3D) genome architecture, this manuscript presents a sequence-based deep-learning approach, Orca, that predicts directly from sequence the 3D genome architecture from kilobase to whole-chromosome scale. Orca captures the sequence dependencies of structures including chromatin compartments and topologically associating domains, as well as diverse types of interactions from CTCF-mediated to enhancer–promoter interactions and Polycomb-mediated interactions with cell-type specificity. Orca enables various applications including predicting structural variant effects on multiscale genome organization and it recapitulated effects of experimentally studied variants at varying sizes (300 bp to 90 Mb). Moreover, Orca enables in silico virtual screens to probe the sequence basis of 3D genome organization at different scales. At the submegabase scale, it predicted specific transcription factor motifs underlying cell-type-specific genome interactions. At the compartment scale, virtual screens of sequence activities suggest a model for the sequence basis of chromatin compartments with a prominent role of transcription start sites. Orca is a sequence-based deep-learning algorithm that predicts 3D genome architecture from kilobase to whole-chromosome scale, including the impact of structural variants. In silico modeling identifies a putative sequence basis for chromatin compartment formation.

Hepatocellular carcinoma (HCC) is the most frequent subtype of primary liver cancer and one of the leading causes of cancer-related death worldwide. However, the molecular mechanisms underlying HCC pathogenesis have not been fully understood. Emerging evidences have recently suggested the crucial role of long noncoding RNAs (lncRNAs) in the tumorigenesis and progression of HCC. Various HCC-related lncRNAs have been shown to possess aberrant expression and participate in cancerous phenotypes (e.g. persistent proliferation, evading apoptosis, accelerated vessel formation and gain of invasive capability) through their binding with DNA, RNA or proteins, or encoding small peptides. Thus, a deeper understanding of lncRNA dysregulation would provide new insights into HCC pathogenesis and novel tools for the early diagnosis and treatment of HCC. In this review, we summarize the dysregulation of lncRNAs expression in HCC and their tumor suppressive or oncogenic roles during HCC tumorigenesis. Moreover, we discuss the diagnostic and therapeutic potentials of lncRNAs in HCC.

Receptor-like kinases (RLKs) and Receptor-like proteins (RLPs) play crucial roles in plant immunity, growth, and development. Plants deploy a large number of RLKs and RLPs as pattern recognition receptors (PRRs) that detect microbe- and host-derived molecular patterns as the first layer of inducible defense. Recent advances have uncovered novel PRRs, their corresponding ligands, and mechanisms underlying PRR activation and signaling. In general, PRRs associate with other RLKs and function as part of multiprotein immune complexes at the cell surface. Innovative strategies have emerged for the rapid identification of microbial patterns and their cognate PRRs. Successful pathogens can evade or block host recognition by secreting effector proteins to “hide” microbial patterns or inhibit PRR-mediated signaling. Furthermore, newly identified pathogen effectors have been shown to manipulate RLKs controlling growth and development by mimicking peptide hormones of host plants. The ongoing studies illustrate the importance of diverse plant RLKs in plant disease

• Plants use their innate immune system to defend against phytopathogens. As a part of this, pattern triggered-immunity is activated via pattern recognition receptor (PRR) detection of pathogen-associated molecular patterns (PAMPs). Although an increasing number of PAMPs have been identified, the PRRs for their recognition remain largely unknown. • In the present study, we report a receptor-like protein RE02 (Response to VmE02) in Nicotiana benthamiana, which mediates the perception of VmE02, a PAMP previously identified from the phytopathogenic fungus Valsa mali, using virus-induced gene silencing (VIGS), co-immunoprecipitation, pull-down and microscale thermophoresis assays. • We show that silencing of RE02 markedly attenuated VmE02-triggred cell death and immune responses. RE02 specifically interacted with VmE02 in vivo and in vitro, and it displayed a high affinity for VmE02. Formation of a complex with the receptor-like kinases SOBIR1 and BAK1 was essential for RE02 to perceive VmE02. Moreover, RE02-silenced plants exhibited enhanced susceptibility to both the oomycete Phytophthora capsici and the fungus Sclerotinia sclerotiorum, while overexpression of RE02 increased plant resistance to these pathogens. • Together, our results indicate that the PAMP VmE02 and the receptor-like protein RE02 represent a new ligand–receptor pair in plant immunity, and that RE02 represents a promising target for engineering disease resistance.

Light–matter interaction is one of the key routes to understanding and manipulating geometric and electronic behaviors of materials, especially two-dimensional materials which are optically accessible owing to their high surface to volume ratio. In the current work, we focus on the recently discovered two-dimensional sliding ferroelectric materials, in which the out-of-plane electric polarization can be switched with a small horizontal translation in one layer. Combining symmetry analysis and first-principles calculations, we predict that light illumination could inject non-equilibrium magnetic moments into the sliding ferroelectrics. Such magnetic moment is composed of both spin and orbital degrees of freedom contributions. We use ZrI2, WTe2, and MoS2 bilayer ferroelectrics to illustrate our theory. Under intermediate light illumination, one can yield non-equilibrium magnetic moments on the order of 0.1–1 μB in these systems, which also depends on the polarization nature of incident light. Furthermore, we show that such photo-injected magnetism changes its sign when the sliding dipole moment switches. This photo-magnetization can be detected by magneto-optical methods (such as Kerr or Faraday effect), which serves as an indicator of sliding ferroelectricity. Hence, one can use an all-optical pump and probe setup to measure and detect the subtle sliding ferroelectric phase.

Environmental degradations are mainly caused by the use of different energy resources for economic growth. This research examined the influence of energy consumption (coal consumption, oil consumption, and gas consumption) and economic growth on environmental degradation in Pakistan. This research used newly developed method dynamic ARDL simulations to scrutinize the actual influence of positive and negative change in the use of coal consumption, oil consumption, and gas consumption for energy and economic growth on environmental degradation in Pakistan. The examined results of dynamic ARDL indicate that economic growth, coal consumption, oil consumption, and natural gas consumption have positive impact on the environmental degradations in Pakistan both in short run and long run. It is suggested that environmental degradations can be reduced by promoting renewable energy sources for energy.

The plant immune system is fundamental for plant survival in natural ecosystems and for productivity in crop fields. Substantial evidence supports the prevailing notion that plants possess a two-tiered innate immune system, called pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). PTI is triggered by microbial patterns via cell surface-localized pattern-recognition receptors (PRRs), whereas ETI is activated by pathogen effector proteins via predominantly intracellularly localized receptors called nucleotide-binding, leucine-rich repeat receptors (NLRs) 1 – 4 . PTI and ETI are initiated by distinct activation mechanisms and involve different early signalling cascades 5 , 6 . Here we show that Arabidopsis PRR and PRR co-receptor mutants— fls2 efr cerk1 and bak1 bkk1 cerk1 triple mutants—are markedly impaired in ETI responses when challenged with incompatible Pseudomonas syrinage bacteria. We further show that the production of reactive oxygen species by the NADPH oxidase RBOHD is a critical early signalling event connecting PRR- and NLR-mediated immunity, and that the receptor-like cytoplasmic kinase BIK1 is necessary for full activation of RBOHD, gene expression and bacterial resistance during ETI. Moreover, NLR signalling rapidly augments the transcript and/or protein levels of key PTI components. Our study supports a revised model in which potentiation of PTI is an indispensable component of ETI during bacterial infection. This revised model conceptually unites two major immune signalling cascades in plants and mechanistically explains some of the long-observed similarities in downstream defence outputs between PTI and ETI. Bacteria elicit two distinct immune responses in Arabidopsis thaliana , mediated by diverse signalling receptors but working in a synergistic manner.

Background METTL3 is known to be involved in all stages in the life cycle of RNA. It affects the tumor formation by the regulation the m6A modification in the mRNAs of critical oncogenes or tumor suppressors. In bladder cancer, METTL3 could promote the bladder cancer progression via AFF4/NF-κB/MYC signaling network by an m6A dependent manner. Recently, METTL3 was also found to affect the m6A modification in non-coding RNAs including miRNAs, lincRNAs and circRNAs. However, whether this mechanism is related to the proliferation of tumors induced by METTL3 is not reported yet. Methods Quantitative real-time PCR, western blot and immunohistochemistry were used to detect the expression of METTL3 in bladder cancer. The survival analysis was adopted to explore the association between METTL3 expression and the prognosis of bladder cancer. Bladder cancer cells were stably transfected with lentivirus and cell proliferation and cell cycle, as well as tumorigenesis in nude mice were performed to assess the effect of METTL3 in bladder cancer. RNA immunoprecipitation (RIP), co-immunoprecipitations and RNA m6A dot blot assays were conducted to confirm that METTL3 interacted with the microprocessor protein DGCR8 and modulated the pri-miR221/222 process in an m6A-dependent manner. Luciferase reporter assay was employed to identify the direct binding sites of miR221/222 with PTEN. Colony formation assay and CCK8 assays were conducted to confirm the function of miR-221/222 in METTL3-induced cell growth in bladder cancer. Results We confirmed the oncogenic role of METTL3 in bladder cancer by accelerating the maturation of pri-miR221/222, resulting in the reduction of PTEN, which ultimately leads to the proliferation of bladder cancer. Moreover, we found that METTL3 was significantly increased in bladder cancer and correlated with poor prognosis of bladder cancer patients. Conclusions Our findings suggested that METTL3 may have an oncogenic role in bladder cancer through interacting with the microprocessor protein DGCR8 and positively modulating the pri-miR221/222 process in an m6A-dependent manner. To our knowledge, this is the first comprehensive study that METTL3 affected the tumor formation by the regulation the m6A modification in non-coding RNAs, which might provide fresh insights into bladder cancer therapy.

Very recently, a novel two-dimension (2D) MXene, MoSi2N4, was successfully synthesized with excellent ambient stability, high carrier mobility, and moderate band gap (2020 Science 369 670). In this work, the intrinsic lattice thermal conductivity of monolayer MoSi2N4 is predicted by solving the phonon Boltzmann transport equation based on the first-principles calculations. Despite the heavy atomic mass of Mo and complex crystal structure, the monolayer MoSi2N4 unexpectedly exhibits a quite high lattice thermal conductivity over a wide temperature range between 300 to 800 K. At 300 K, its in-plane lattice thermal conductivity is 224 Wm−1 K−1. The detailed analysis indicates that the large group velocities and small anharmonicity are the main reasons for its high lattice thermal conductivity. We also calculate the lattice thermal conductivity of monolayer WSi2N4, which is only a little smaller than that of MoSi2N4. Our findings suggest that monolayer MoSi2N4 and WSi2N4 are potential 2D materials for thermal transport in future nano-electronic devices.

Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.