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Glioblastoma multiforme (GBM) is acknowledged as the most aggressive primary brain tumor in adults. It is typically characterized by the high heterogeneity which corresponds to extensive genetic mutations and complex alternative splicing (AS) profiles. Known as a major repressive splicing factor in AS, polypyrimidine tract-binding protein 1 (PTBP1) is involved in the exon skipping events of multiple precursor mRNAs (pre-mRNAs) in GBM. However, precise mechanisms that modulate the expression and activity of PTBP1 remain to be elucidated. In present study, we provided evidences for the role of a long intergenic noncoding RNA (LINREP) implicated in the regulation of PTBP1-induced AS. LINREP interacted with PTBP1 and human antigen R (HuR, ELAVL1) protein complex and protected PTBP1 from the ubiquitin-proteasome degradation. Consequently, a broad spectrum of PTBP1-induced spliced variants was generated by exon skipping, especially for the skipping of reticulon 4 (RTN4) exon 3. Interestingly, LINREP also promoted the dissociation of nuclear UPF1 from PTBP1, which increased the binding of PTBP1 to RTN4 transcripts, thus enhancing the skipping of RTN4 exon 3 to some extent. Besides, HuR recruitment was essential for the stabilization of LINREP via a manner dependent on N

High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation.

High-dimensional cellular and molecular profiling of human samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate predictive biomarkers and prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions and their integration to infer causal relationships. Here we present Essential Regression (ER), an interpretable machine learning approach for high-dimensional multi-omic datasets, that addresses these problems by identifying latent factors and their likely cause-effect relationships with the system-wide outcome/properties of interest. ER is a novel data-distribution-free latent-factor regression model that integrates multi-omic datasets and identifies latent factors significantly associated with an outcome. ER outperforms a range of state-of-the-art methods in terms of prediction performance on simulated datasets. ER can be coupled with probabilistic graphical modeling thereby strengthening the causal inferences. ER generates novel cellular and molecular predictions, using multi-omic human systems immunology datasets, pertaining to immunosenescence and immune dysregulation.

Summary It has been a long pursuit for the adoption of the lithium (Li) metal anode because of its extremely high specific capacity and the lowest electrochemical equilibrium potential. However, the practical implement of Li anode is severely hindered by the unstable interfaces stemming from its ultrahigh reactivity, which directly dictates a low Coulombic efficiency, dendrite growth behavior, and even safety concerns. In this review, we focus on the emerging strategy of artificial protective films to fulfill a stable working Li metal anode. The recent efforts on strengthening the Li metal and liquid or solid electrolyte interfaces with artificial films are comprehensively summarized and discussed. This review highlights the significance of interface-related science and engineering in both liquid-state and solid-state Li metal batteries, affords fresh insights on the interdisciplinary issues, and calls on more dedication to pave the way for safe and high-energy-density Li metal batteries.

Alkali metal electrodes (including lithium (Li), sodium (Na), and potassium (K)) have been strongly considered as promising candidates for next-generation batteries, beyond lithium ion batteries, b...

The stability of a battery is strongly dependent on the feature of solid electrolyte interphase (SEI). The electrical double layer forms prior to the formation of SEI at the interface between the Li metal anode and the electrolyte. The fundamental understanding on the regulation of the SEI structure and stability on Li surface through the structure of the electrical double layer is highly necessary for safe batteries. Herein, the interfacial chemistry of the SEI is correlated with the initial Li surface adsorption electrical double layer at the nanoscale through theoretical and experimental analysis. Under the premise of the constant solvation sheath structure of Li+ in bulk electrolyte, a trace amount of lithium nitrate (LiNO3) and copper fluoride (CuF2) were employed in electrolytes to build robust electric double layer structures on a Li metal surface. The distinct results were achieved with the initial competitive adsorption of bis(fluorosulfonyl)imide ion (FSI-), fluoride ion (F-), and nitrate ion (NO3-) in the inner Helmholtz plane. As a result, Cu-NO3- complexes are preferentially adsorbed and reduced to form the SEI. The modified Li metal electrode can achieve an average Coulombic efficiency of 99.5% over 500 cycles, enabling a long lifespan and high capacity retention of practical rechargeable batteries. The as-proposed mechanism bridges the gap between Li+ solvation and the adsorption about the electrode interface formation in a working battery.

BACKGROUND This study aimed to use gas chromatography-mass spectrometry (GC-MS) to identify the chemical constituents of volatile oil extracted by steam distillation from Cichorium glandulosum Boiss et Huet (CG), a traditional Uyghur medicine, and to investigate its effects on carbon tetrachloride (CCl₄)-induced hepatic fibrosis in rats. MATERIAL AND METHODS Sprague-Dawley rats (n=60) included six groups: the control group (n=10), untreated model group (n=10), the volatile oil of CG high-dose group (0.15 ml/kg) (n=10), the volatile oil of CG medium-dose group (0.10 ml/kg) (N=10), the volatile oil of CG low-dose group (0.05 ml/kg) (n=10), and the silybin-treated group (0.20 ml/kg) (n=10). Rats given the essential oil extract of CG by intragastric administration, and then subcutaneously injected with a solution of CCl₄ in olive oil to create the rat model of hepatic fibrosis. Serum samples were analyzed for markers of liver function, including aspartate transaminase (AST), alanine transaminase (ALT), malondialdehyde (MDA), hydroxyproline (Hyp), γ-glutamyl transpeptidase (γ-GT), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), and albumin (Alb). Histology and immunohistochemistry were performed on rat liver tissue. RESULTS Thirty-eight compounds were identified from the volatile oil of CG (total, 98.058%), with terpenoids, including citronellol, being the most abundant. In the animal model of liver fibrosis, all doses of volatile oil of CG significantly reduced the serum levels of AST, ALT, MDA, Hyp, γ-GT, LDH, ALP, and Alb. CONCLUSIONS GC-MS identified the components of the volatile oil of CG, which included citronellol. Treatment with volatile oil of CG reduced liver fibrosis in a rat model.

Summary LOVE, a robust, scalable latent model-based clustering method for biological discovery, can be used across a range of datasets to generate both overlapping and non-overlapping clusters. In our formulation, a cluster comprises variables associated with the same latent factor and is determined from an allocation matrix that indexes our latent model. We prove that the allocation matrix and corresponding clusters are uniquely defined. We apply LOVE to biological datasets (gene expression, serological responses measured from HIV controllers and chronic progressors, vaccine-induced humoral immune responses) resulting in meaningful biological output. For all three datasets, the clusters generated by LOVE remain stable across tuning parameters. Finally, we compared LOVE's performance to that of 13 state-of-the-art methods using previously established benchmarks and found that LOVE outperformed these methods across datasets. Our results demonstrate that LOVE can be broadly used across large-scale biological datasets to generate accurate and meaningful overlapping and non-overlapping clusters., Graphical Abstract, Highlights • LOVE is a robust, scalable, and versatile latent model-based clustering method • Has theoretical guarantees, and can generate overlapping and non-overlapping clusters • Generates meaningful clusters from datasets spanning a range of biological domains • Using established benchmarks, outperforms 13 state-of-the-art methods across datasets, Biological Sciences, Bioinformatics, Statistical Computing

Thin artificial solid electrolyte coatings are effective to enhance the electrochemical performances and safety issues of lithium (Li) metal anode. However, massive and efficient fabrication of artificial protection layers on Li metal anode surface remains challenging. Herein, we describe a sandwiched Li metal anode fabricated through a continuous roll to roll calendering method to implant a thin and large-area carbon layer on Li metal anode surface at room temperature. Specifically, a carbon layer (~ 3 μm in thickness) can be entirely grafted from Cu substrate to 50 μm Li belt surface due to the stickiness of metallic Li. The carbon layer not only plays a critical role in providing rich nucleation sites for Li plating, but more importantly diminishes the metallurgical nonuniformity effects (slip lines) on stripping. Therefore, even Li plating/stripping morphologies are achieved and the as-obtained sandwiched Li/C composite anodes exhibit improved cycling stability both in Li | LiFePO4 and Li | S coin cells and pouch cells. This continuous roll to roll calendering strategy opens a new avenue for grafting various thin artificial protection layers on Li metal surface for safe rechargeable batteries.

Safe lithium (Li) metal batteries have been plagued by dendrite growth due to a heterogeneous solid electrolyte interphases (SEI) on the Li metal anode. Modulating the solvation sheath of Li ions enhances the uniformity and stability of SEI significantly. However, anion regulation in the solvation sheath for constructing stable SEI is rarely touched. Herein, the solvation structure of original bis(fluorosulfonyl)imide (FSI–) anions in the solvation sheath is altered by introduction of other anions, promoting the complete decomposition of FSI– and forming a stable SEI on the Li metal anode. Moreover, both the oxidation stability window of the electrolyte and current collector protection were enhanced. Furthermore, the components and structure of the solvation sheath were disclosed by combining 17O nuclear magnetic resonance and molecular dynamics simulations. This work provides fresh insight into the interrelation among various anions on regulating the solvation sheath of Li ions and demonstrates guidance ...

Three-dimensional (3D), high-specific-surface-area, and porous current collectors are strongly considered as the hosts of lithium deposition to avoid dendrite growth of lithium metal in rechargeable batteries. However, a major hurdle in these hosts is the poor affinity of lithium in non-polar framework and favorable lithium deposition toward the conductive separator-facing surface while leaving the interior voids empty. Herein, we demonstrate an effective strategy to address the issue of spatially heterogeneous lithium deposition in 3D Janus current collectors by modifying its separator-away surface with low Li/Li+ over-potential nanoparticles as nucleation sites to guide lithium deposition. The metallic lithium preferentially nucleates around the gold nanoparticles that are sputtered on the separator-away surface of the carbon paper. The lithium metal then grows along the adjacent carbon fiber and renders it spatially homogeneous for deposition/dissolution during the repeated charge/discharge processes. The Janus gold nanoparticle-modified carbon paper (Au/CP) electrode exhibits an excellent Coulombic efficiency of 99.1% over 100 cycles at 1.0 mA cm⁻2 in the ether electrolyte, while the pristine carbon paper (CP) and stainless steel foil (SS) electrodes exhibit Coulombic efficiencies of less than 80.0% after 74 and 59 cycles, respectively. The strategy is universal and similar results are obtained when replacing gold, carbon paper, and ether electrolyte with zinc oxide, nickel foam, and carbonate electrolyte, respectively. Therefore, this strategy presents a general approach to regulate lithium ion distribution, nucleation, and deposition behavior for long-lifespan lithium metal batteries.

We propose advanced image enhancement algorithms for conventional ghost imaging. The algorithms are based on modulating the bucket signals to enlarge their divergence. In this paper, we adopt three mathematical functions: power function like f ( B ) = B n , exponential function like f ( B ) = exp ( p ⋅ B ) , and self modulation function like f ( B ) = B q ⋅ B , where B is the bucket intensity in the object path. Our experimental results demonstrate the enhancement of the contrast of the correlated images with these algorithms.

Lithium metal batteries (LMBs) have attracted increasing attentions for their ultrahigh specific capacity (3860 mAh g−1) and the lowest electrode potential (−3.04 V vs. standard hydrogen electrode). However, the dynamic volume changes, the complex interfacial reactions, and the dendrite growth remain as the grand challenges in LMBs that prevent their practical applications. A bi-layer artificial solid electrolyte interphase (BL-SEI), which is composed of covalent graphitic materials (graphene and h-BN) and inorganic components (LiF, Li2O, Li3N, and Li2CO3), is rationally designed through comprehensive first-principles calculation to render a stable Li metal anode. Key interfacial properties, such as chemical stability, ionic conductivity, and mechanical strength, are systematically investigated to achieve a rational design of the BL-SEI. Among all the considered BL-SEI, the graphene/LiF combination is hopeful to exhibit the best interfacial stability and electrochemical performance. The protective role of BL-SEI for Li metal anode comes from the coupled effects through the anisotropic character and the defective structure. This work reveals the origin of the significant role of BL-SEI for achieving a stable Li metal anode from the atomic and electronic level, affording a paradigm for rational deign of a high-performance artificial SEI in working LMBs.

Summary Because of the high capacity of lithium (Li) metal and the intrinsic safety of solid-state electrolyte, solid-state Li-metal batteries are regarded as a promising candidate for next-generation energy storage. However, uncontrollable dendrite growth and large interfacial resistance severely hamper the practical applications. This review summarizes the issues generated by the marriage of Li-metal anodes and solid-state electrolytes. First, the current challenges are underscored. Specific attention is paid to the large interfacial resistance, uncontrolled dendrite growth, and low operation current or capacity. The second section is dedicated primarily to understanding the ionic channels in the composite electrolyte and the space charge layers in the interfacial region. Based on these dilemmas and working principles, emerging strategies to render solid-state Li-metal batteries are summarized. Finally, the general conclusion and perspective on the current limitations and recommended research of solid-state Li-metal batteries are presented.

Lithium (Li) metal is regarded as a “Holy Grail” anode for next-generation high-energy-density rechargeable batteries due to its high volumetric (2046 mA h cm−3) and gravimetric specific capacity (3862 mA h g−1) as well as the lowest reduction potential (−3.04 V vs. standard hydrogen electrode). However, undesirable dendrite growth and repeated destruction/formation of the solid electrolyte interphase (SEI) on Li metal anode during the long-term charging/discharging cycles have limited the practical applications of Li metal batteries. In this review, we summarize the strategies to restrain Li dendrites through electrolyte modification, multifunctional barriers, composite metallic lithium electrode, and 3D current collectors. The Li metal anode protection can be achieved by efficiently regulating the diffusion and distribution behavior of Li ions and electrons. The further exploration on rational integration of these strategies is highly expected to afford more fundamental understanding and engineering applications to practical Li metal batteries.