CCN1, a Pro-Inflammatory Factor, Aggravates Psoriasis Skin Lesions by Promoting Keratinocyte Activation

Psoriasis is a common chronic skin disease characterized by epidermal hyperplasia and inflammation. The pathogenesis of psoriasis is multifactorial and is not fully understood. Here we demonstrate that CCN1 (also called Cyr61, which is short for cysteine-rich 61), an extracellular matrix protein that is also considered a pro-inflammatory factor, is highly expressed in the lesional skin of psoriasis patients, as well as in that of imiquimod (IMQ)- and IL-23-treated psoriasis-like mice. Then we show that blocking CCN1 function in vivo attenuates epidermal hyperplasia and inflammation in psoriasis-like mice. Further, in primary cultured normal human keratinocytes and HaCaT (human keratinocyte cell line) cells, CCN1 promotes keratinocyte activation, including the proliferation and expression of immune-related molecules. Finally, we observe that integrin α6β1 is the receptor of CCN1 in keratinocytes, and CCN1 stimulation activates the downstream phosphoinositide-3 kinase/Akt/NF-κB signaling pathway. Taken together, our findings reveal that CCN1 has a critical role in psoriasis pathogenesis. Moreover, as CCN1 is a secreted extracellular matrix (ECM) protein, our study also provides evidence that ECM, which is involved in psoriatic pathogenesis, could be a potent target for psoriasis treatment

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure

Multi-Physics Multi-Objective Optimal Design of Bearingless Switched Reluctance Motor Based on Finite-Element Method

The bearingless switched reluctance motor (BSRM) integrates the switched reluctance motor (SRM) with the magnetic bearings, which avoids mechanical bearings-loss and makes it promising in high-speed applications. In this paper, a comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on finite-element method (FEM) is proposed. At first, the 2-D electromagnetic model of a fabricated initial design prototype is built and solved by the open-source FEM software, Elmer. The iron loss model in Elmer based on the Fourier series is modified by a transient iron loss model with less computation time. Besides, a simplified lumped-parameter (LP) thermal model of the BSRM is applied to estimate the temperature rise of BSRM in the steady state. Then, the comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on FEM is proposed. The objectives, constraints, and decision variables for optimization are determined. The multi-objective genetic particle swarm optimizer is utilized to obtain the Pareto front of optimization. The electromagnetic performance of the final optimal design is compared with the initial design. Comparison results show that the average electromagnetic torque and the efficiency are significantly enhanced

Numerical Research on the Mixture Mechanism of Polluted and Fresh Air at the Staggered Tunnel Portals

In longitudinal ventilation, circulating air is formed in portals for closely spaced twin tunnels, which causes mixing between the polluted air exhausted from one tunnel and the fresh air flow of another tunnel, thus leading to the rising costs of ventilation system construction and operation. In this study, for the closely spaced tunnel with staggered inlet and outlet, the computational fluid dynamics (CFD) numerical simulation method was adopted to reveal flow characteristics of the circulating air as well as variation rules of the circulating air mixing ratio φc with tunnel structure and operation parameters. Results show that both reducing inlet air velocity and increasing outlet air velocity and lateral distance can reduce the impact of the negative-pressure zone at the tunnel entrance on the jet flow structure at the tunnel exit, thus weakening the circulating air. When the inlet is placed behind or aligned with the outlet (staggered distance ∆l ≤ 0), φc will increase linearly along with the increase of staggered distance; when the inlet is placed before the outlet (∆l > 0), φc will first increase and then decrease with the increase of staggered distance. An expression to predict circulating air mixing ratio was created by sections. The predictions show a good correlation with the measurements and indicate that the front slope gradient of the tunnel portal is also one of the factors affecting the circulating air mixing ratio

Soft Actor–Critic-Driven Adaptive Focusing under Obstacles

Electromagnetic (EM) waves that bypass obstacles to achieve focus at arbitrary positions are of immense significance to communication and radar technologies. Small-sized and low-cost metasurfaces enable the accomplishment of this function. However, the magnitude-phase characteristics are challenging to analyze when there are obstacles between the metasurface and the EM wave. In this study, we creatively combined the deep reinforcement learning algorithm soft actor–critic (SAC) with a reconfigurable metasurface to construct an SAC-driven metasurface architecture that realizes focusing at any position under obstacles using real-time simulation data. The agent learns the optimal policy to achieve focus while interacting with a complex environment, and the framework proves to be effective even in complex scenes with multiple objects. Driven by real-time reinforcement learning, the knowledge learned from one environment can be flexibly transferred to another environment to maximize information utilization and save considerable iteration time. In the context of future 6G communications development, the proposed method may significantly reduce the path loss of users in an occluded state, thereby solving the open challenge of poor signal penetration. Our study may also inspire the implementation of other intelligent devices

Ultra-Broadband and Compact TM-Pass Polarizer Based on Graphene-Buried Polymer Waveguide

We report an ultra-broadband and compact TM-pass polarizer based on graphene-buried polymer waveguides. The characteristic parameters of the polarizer were carefully designed and optimized. The standard microfabrication processes were employed to fabricate the device. The presented polarizers exhibit high polarization-dependent transmission imposing a TE mode cutoff while leaving the TM mode almost unaffected. We experimentally demonstrated the polarizer that has an ultra-high extinction ratio of more than 22.9 dB and 41.9 dB for the monolayer graphene film placed on the surface of core layer and buried in the center of core layer, respectively, and as low insertion loss as ~4.0 dB for the TM mode with the bandwidth over 110 nm. The presented polarizer has the advantages of high extinction ratio, ultra-broadband, low cost, and easy integration with other polymer-based planar lightwave devices

Physcomitrella Patens Dehydrins (PpDHNA and PpDHNC) Confer Salinity and Drought Tolerance to Transgenic Arabidopsis Plants

Dehydrins (DHNs) as a member of late-embryogenesis-abundant (LEA) proteins are involved in plant abiotic stress tolerance. Two dehydrins PpDHNA and PpDHNC were previously characterized from the moss Physcomitrella patens, which has been suggested to be an ideal model plant to study stress tolerance due to its adaptability to extreme environment. In this study, functions of these two genes were analyzed by heterologous expressions in Arabidopsis. Phenotype analysis revealed that overexpressing PpDHN dehydrin lines had stronger stress resistance than wild type and empty-vector control lines. These stress tolerance mainly due to the up-regulation of stress-related genes expression and mitigation to oxidative damage. The transgenic plants showed strong scavenging ability of reactive oxygen species(ROS), which was attributed to the enhancing of the content of antioxidant enzymes like superoxide dismutase (SOD) and catalase (CAT). Further analysis showed that the contents of chlorophyll and proline tended to be the appropriate level (close to non-stress environment) and the malondialdehyde (MDA) were repressed in these transgenic plants after exposure to stress. All these results suggest the PpDHNA and PpDHNC played a crucial role in response to drought and salt stress