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Previous research has acknowledged that climate change is likely to expand the wealth gap, and climate policies may further increase inequality. Nevertheless, little research has focused on how climate policies affect inequality. To address this, we employ a Computable General Equilibrium (CGE) model to quantify the inequality impacts of the Chinese carbon taxes. Our CGE model results show that tax impacts on inequality are influenced by distribution of climate damages, tax payments, and recycling of tax revenues. Specifically, a positive correlation between income and climate damage induces lower inequality, compared to a zero or negative correlation. Tax payments by high-income households induce lower inequality than tax payments proportional to or independent from income. Recycling tax revenues to low-income households only induces lower inequality than the other recycling schemes. The results imply that relative utility is determined by absolute income, whereas income inequality only has a slight impact on it. In other words, governments could reduce negative feelings about inequality under a climate policy by increasing national income, even if the climate policy may induce higher inequality.

Despite the significant impacts of technology on the socioeconomic effects of climate policies, many previous researchers neglected the induced technical impacts and thus resulted in biased evaluations of climate policies. Hence, it is important that the induced technology should be endogenized in the policy evaluation framework. The purpose of this paper is the quantification of the technical impacts of the Chinese carbon tax using a Computable General Equilibrium (CGE) model. The technical impacts are denoted by the induced technological change (ITC), which is a function of the energy-use efficiency (EUE), energy-production efficiency (EPE), and nonenergy-production efficiency (ENE). The carbon tax will increase the energy cost share because of the internalisation of the abatement costs. This paper empirically shows that the carbon tax will decrease the energy cost share and production efficiency but increase the energy use and nonenergy production efficiency. Under the carbon tax, the ITC will decrease the energy use and production efficiency but increase the nonenergy production efficiency. The ITC will increase the RGDP, decrease the household welfare, and increase the average social cost of carbon (ASCC). This finding implies that the ITC of the carbon tax is biased towards the technical progress of nonenergy sectors; the emission abatement will become costlier under the ITC impacts. Although the quantification method of the technical impacts was from an existing published paper, the CGE analysis of the ITC impacts of the carbon tax in China is original in this paper.

Emission trading scheme (ETS) is popular to abate anthropogenic emissions throughout the world. Previous researchers focused on evaluating ETS policy effect, but ETS design is usually neglected because ETS is already mostly sophisticated worldwide. This is not the case in China, as the Chinese nationwide ETS (CNETS) came into effect in July 2021. Implemented for a brief period, the CNETS lacks implementation details and thus may not achieve mitigation targets cost-effectively. In this paper, we attempt to narrow the research gap by comprehensively designing the CNETS. Our research framework is based on a dynamic recursive computable general equilibrium (CGE) model. The CGE model results show that the appropriate CNETS should include the coverage of the electricity generation and manufacturing sectors, higher carbon price (175${CNY} / {t} {CO}_2$ ), quota allocation based on the carbon intensity in the previous year, higher quota decline factor (2%) and time-decreasing free quota ratio. Although we have only designed the Chinese ETS in this paper, the research framework may become a paradigm of designing appropriate ETS.

Fast and precise robot motion is needed in many industrial applications. Most industrial robot motion controllers allow externally commanded motion profiles, but the trajectory tracking performance is affected by the robot dynamics and joint servo controllers, to which users have no direct access and about which they have little information. The performance is further compromised by time delays in transmitting the external command as a setpoint to the inner control loop. This paper presents an approach for combining neural networks and iterative learning controls to improve the trajectory tracking performance for a multi-axis articulated industrial robot. For a given desired trajectory, the external command is iteratively refined using a high-fidelity dynamical simulator to compensate for the robot inner-loop dynamics. These desired trajectories and the corresponding refined input trajectories are then used to train multi-layer neural networks to emulate the dynamical inverse of the nonlinear inner-loop dynamics. We show that with a sufficiently rich training set, the trained neural networks generalize well to trajectories beyond the training set as tested in the simulator. In applying the trained neural networks to a physical robot, the tracking performance still improves but not as much as in the simulator. We show that transfer learning effectively bridges the gap between simulation and the physical robot. Finally, we test the trained neural networks on other robot models in simulation and demonstrate the possibility of a general purpose network. Development and evaluation of this methodology are based on the ABB IRB6640-180 industrial robot and ABB RobotStudio software packages.

Polycomb repressive complex 2 (PRC2) silences gene expression through trimethylation of K27 of histone H3 (H3K27me3) via its catalytic SET domain. A missense mutation in the substrate of PRC2, histone H3K27M, is associated with certain pediatric brain cancers and is linked to a global decrease of H3K27me3 in the affected cells thought to be mediated by inhibition of PRC2 activity. We present here the crystal structure of human PRC2 in complex with the inhibitory H3K27M peptide bound to the active site of the SET domain, with the methionine residue located in the pocket that normally accommodates the target lysine residue. The structure and binding studies suggest a mechanism for the oncogenic inhibition of H3K27M. The structure also reveals how binding of repressive marks, like H3K27me3, to the EED subunit of the complex leads to enhancement of the catalytic efficiency of the SET domain and thus the propagation of this repressive histone modification. Polycomb repressive complex 2 (PRC2) silences gene expression through trimethylation of K27 of histone H3 (H3K27Me). Here, the authors report the structure of the human PRC2 complex bound to the oncogenic H3K27M mutant, and suggest a mechanism for its potency in childhood brain cancers.

An important challenge in pre-processing data from droplet-based single-cell RNA sequencing protocols is distinguishing barcodes associated with real cells from those binding background reads. Existing methods test barcodes individually and consequently do not leverage the strong cell-to-cell correlation present in most datasets. To improve cell detection, we introduce CB2, a cluster-based approach for distinguishing real cells from background barcodes. As demonstrated in simulated and case study datasets, CB2 has increased power for identifying real cells which allows for the identification of novel subpopulations and improves the precision of downstream analyses.

Programmed cell death ligand 1 (PD-L1) interacts with programmed cell death protein-1 (PD-1) as an immune checkpoint. Reactivating the immune response by inhibiting PD-L1 using therapeutic antibodies provides substantial clinical benefits in many, though not all, melanoma patients. However, transcriptional suppression of PD-L1 expression as an alternative therapeutic anti-melanoma strategy has not been exploited. Here we provide biochemical evidence demonstrating that ultraviolet radiation (UVR) induction of PD-L1 in skin is directly controlled by nuclear factor E2-related transcription factor 2 (NRF2). Depletion of NRF2 significantly induces tumor infiltration by both CD8 + and CD4 + T cells to suppress melanoma progression, and combining NRF2 inhibition with anti-PD-1 treatment enhanced its anti-tumor function. Our studies identify a critical and targetable PD-L1 upstream regulator and provide an alternative strategy to inhibit the PD-1/PD-L1 signaling in melanoma treatment.

Pulmonary fibrosis (PF) is a disease characterized by dysregulated extracellular matrix deposition and aberrant fibroblast activation. Emerging evidence implicates that dysregulated copper metabolism contributed to fibrotic pathogenesis, yet its role and the therapeutic potential of copper modulation remain underexplored. This study investigated the involvement of cuproptosis, a programmed cell death induced by intracellular copper overload, in PF and evaluated the therapeutic efficacy of the copper chelator tetrathiomolybdate (TTM). In a bleomycin (BLM)-induced murine PF model, intratracheal BLM administration elevated lung copper levels, upregulated oligomerized DLAT, and exacerbated fibrosis, as evidenced by collagen deposition, α-smooth muscle actin, and transforming growth factor-beta expression. TTM treatment significantly attenuated fibrotic progression, reduced oxidative stress, and suppressed Olig-DLAT accumulation. In vitro, copper ionophores induced cuproptosis in bronchial epithelial cells, characterized by reduced viability, elevated intracellular Cu⁺, and Olig-DLAT aggregation, which were reversed by TTM. Furthermore, TTM mitigated TGF-β-driven epithelial–mesenchymal transition (EMT) and fibroblast-to-myofibroblast transition (FMT), downregulating collagen-1 and restoring E-cadherin expression. These findings establish cuproptosis as a novel mechanistic contributor to PF and highlight TTM’s dual role in restoring copper homeostasis and inhibiting fibrogenic pathways, offering a promising therapeutic strategy for fibrotic lung diseases.

Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies. Spatial transcriptomics experiments profile genome-wide gene expression at localized spots across a tissue. Here, the authors identify spot swapping, an artifact where RNA expressed at one tissue spot binds probes at another, and they propose SpotClean to adjust for it.

Titanium (Ti) has been used for long in dentistry and medicine for implant purpose. During the years, not only the commercially pure Ti but also some alloys such as binary and tertiary Ti alloys were used. The aim of this review is to describe and compare the current literature on binary Ti alloys, including Ti–Zr, Ti–In, Ti–Ag, Ti–Cu, Ti–Au, Ti–Pd, Ti–Nb, Ti–Mn, Ti–Mo, Ti–Cr, Ti–Co, Ti–Sn, Ti–Ge and Ti–Ga, in particular to mechanical, chemical and biological parameters related to implant application. Literature was searched using the PubMed and Web of Science databases, as well as google without limiting the year, but with principle key terms such as ‘ Ti alloy’, ‘binary Ti ’, ‘Ti-X’ (with X is the alloy element), ‘dental implant’ and ‘medical implant’. Only laboratory studies that intentionally for implant or biomedical applications were included. According to available literatures, we might conclude that most of the binary Ti alloys with alloying <20% elements of Zr, In, Ag, Cu, Au, Pd, Nb, Mn, Cr, Mo, Sn and Co have high potential as implant materials, due to good mechanical performance without compromising the biocompatibility and biological behaviour compare to cp-Ti.