The effects of anti-sense interleukin-5 gene transferred by recombinant adeno-associated virus in allergic rats

The accumulation and infiltration of eosinophils in airways is one of the most important characteristics of asthma, and is mediated partly by secretion of IL-5 from Th2 lymphocytes. It is well known that interleukin-5 (IL-5) played an important role in the regulation of eosinophils. In this study, an anti-sense IL-5 gene transferred by recombinant adeno-associated virus (rAAV-ASIL-5) was prepared to transfect allergic rats. It was found that the expression of IL-5 protein in plasma and BALF were inhibited significantly. The rAAV-ASIL-5-mediated suppression of total cell counts in peripheral blood and BALF were also observed. Moreover, rAAV-ASIL-5 remarkably reduced the eosinophil counts in peripheral blood and BALF, as well as the expression of ECP protein in plasma and BALF. The inflammation in lungs of rAAV-ASIL-5 pretreated rats also became slighter when compared with allergic rats. Otherwise, no apparent pathological damage to vital organs of rats was found. In conclusion, recombinant adeno-associated virus-mediated delivery of anti-sense IL-5 gene inhibited the accumulation of eosinophils and the airways inflammation in rat model of allergic asthma via suppressing IL-5 expression. It suggested the feasibility of rAAV-ASIL-5 in the gene therapy for allergic asthma and other eosinophilic diseases

Demystifying the nexus between social media usage and overtourism: evidence from Hangzhou, China

Several destinations across the globe experience the challenge of overtourism. Literature identifies social media as one of the driving factors of overtourism. Although some prior studies have explored the relationship between social media and overtourism, the connection between the two still lacks a strong empirical validation. In light of this, we empirically established the relationship between social media usage (SMU) and overtourism, drawing upon Uses and Gratifications Theory (UGT). Quantitative data were collected from 209 tourists who had visited Hangzhou and analysed using PLS-SEM. Research findings reveal that social media usage contributes to overtourism through the mediating effect of tourist flow concentration, even though the influence is weak. Consequently, only 10.2% of the variance in overtourism is explained by the model, suggesting a weak effect of social media usage on overtourism. Implications and limitations are discussed, and avenues for further research are suggested

Self-Supervised Multi-Modal Sequential Recommendation

With the increasing development of e-commerce and online services, personalized recommendation systems have become crucial for enhancing user satisfaction and driving business revenue. Traditional sequential recommendation methods that rely on explicit item IDs encounter challenges in handling item cold start and domain transfer problems. Recent approaches have attempted to use modal features associated with items as a replacement for item IDs, enabling the transfer of learned knowledge across different datasets. However, these methods typically calculate the correlation between the model's output and item embeddings, which may suffer from inconsistencies between high-level feature vectors and low-level feature embeddings, thereby hindering further model learning. To address this issue, we propose a dual-tower retrieval architecture for sequence recommendation. In this architecture, the predicted embedding from the user encoder is used to retrieve the generated embedding from the item encoder, thereby alleviating the issue of inconsistent feature levels. Moreover, in order to further improve the retrieval performance of the model, we also propose a self-supervised multi-modal pretraining method inspired by the consistency property of contrastive learning. This pretraining method enables the model to align various feature combinations of items, thereby effectively generalizing to diverse datasets with different item features. We evaluate the proposed method on five publicly available datasets and conduct extensive experiments. The results demonstrate significant performance improvement of our method

SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Transfer learning via fine-tuning pre-trained transformer models has gained significant success in delivering state-of-the-art results across various NLP tasks. In the absence of centralized data, Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning. However, due to the limited communication, computation, and storage capabilities of edge devices and the huge sizes of popular transformer models, efficient fine-tuning is crucial to make federated training feasible. This work explores the opportunities and challenges associated with applying parameter efficient fine-tuning (PEFT) methods in different FL settings for language tasks. Specifically, our investigation reveals that as the data across users becomes more diverse, the gap between fully fine-tuning the model and employing PEFT methods widens. To bridge this performance gap, we propose a method called SLoRA, which overcomes the key limitations of LoRA in high heterogeneous data scenarios through a novel data-driven initialization technique. Our experimental results demonstrate that SLoRA achieves performance comparable to full fine-tuning, with significant sparse updates with approximately $\sim 1\%$ density while reducing training time by up to $90\%$

High-efficiency segmented thermoelectric power generation modules constructed from all skutterudites

Development of thermoelectric conversion technology for power generation can alleviate the demand for fossil energy and increase the efficiency of energy utilization. To achieve more efficient heat-to-electric conversion, it is desirable to maximize the figure of merit (zT) over a wide temperature range. Constructing a segmented thermoelectric device by serially connecting materials with high zT at different operating temperatures has been proven feasible. However, the issue of compatibility of different thermoelectric materials and the method of connecting different segments to ensure high interfacial stability remain unsolved. Herein, we demonstrate a full skutterudite-based segmented thermoelectric power generation module. The use of thermoelectric materials from the same parent avoids the difference in thermal expansion coefficients and compatibility factors and allows the preparation of thermoelectric junctions by a one-step sintering process. As a result, a high module efficiency of 10.4% is obtained owing to the rational design of the materials, device geometry, and interfaces and is the highest value among skutterudite-based modules reported so far

Transparent Power-Generating Windows Based on Solar-Thermal-Electric Conversion

Zhang Q, Huang A, Ai X, et al. Transparent Power-Generating Windows Based on Solar-Thermal-Electric Conversion. Advanced Energy Materials . 2021: 2101213.Integrating transparent solar-harvesting systems into windows can provide renewable on-site energy supply without altering building aesthetics or imposing further design constraints. Transparent photovoltaics have shown great potential, but the increased transparency comes at the expense of reduced power-conversion efficiency. Here, a new technology that overcomes this limitation by combining solar-thermal-electric conversion with a material's wavelength-selective absorption is presented. A wavelength-selective film consisting of Cs0.33WO3 and resin facilitates high visible-light transmittance (up to 88%) and outstanding ultraviolet and infrared absorbance, thereby converting absorbed light into heat without sacrificing transparency. A prototype that couples the film with thermoelectric power generation produces an extraordinary output voltage of approximate to 4 V within an area of 0.01 m(2) exposed to sunshine. Further optimization design and experimental verification demonstrate high conversion efficiency comparable to state-of-the-art transparent photovoltaics, enriching the library of on-site energy-saving and transparent power generation

Boosting Oxygen and Peroxide Reduction Reactions on PdCu Intermetallic Cubes

Palladium‐based nanocatalysts have the potential to replace platinum‐based catalysts for fuel‐cell reactions in alkaline electrolytes, especially PdCu intermetallic nanoparticles with high electrochemical activity and stability. However, unlike the synthetic methods for obtaining the nanoparticles, the effect of PdCu shape on the performance is relatively less well studied. Here, we demonstrate the facet dependence of PdCu intermetallics on the oxygen reduction reaction (ORR) and peroxide reduction, and reveal that the {100} dominant PdCu cubes have a much higher ORR mass activity and specific activity than spheres at 0.9 V vs. RHE, which is four and five times that of commercial Pd/C and Pt/C catalysts, respectively, and show only a 31.7 % decay after 30 000 cycles in the stability test. Moreover, cubic PdCu nanoparticles show higher peroxide electroreduction activity than Pd cubes and PdCu spheres. Density functional theory (DFT) calculation reveals that the huge difference originates from the reduction in oxygen adsorption energy and energy barrier of peroxide decomposition on the ordered {100} PdCu surface. Given the relationship between the shape and electrochemical performance, this study will contribute to further research on electrocatalytic improvements of catalysts in alkaline environments.Shape the future: PdCu intermetallic cubes and spheres are synthesized to investigate the facet dependence on the oxygen reduction reaction and peroxide reduction. The cubes show large improvements in mass activity towards both reactions, compared with the spheres. DFT calculation uncovers that the dominant {100} faces of the cubes offer more appropriate oxygen adsorption and are thermodynamically favorable for peroxide reduction compared to the surface of spheres.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/1/celc202000381.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/2/celc202000381_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155903/3/celc202000381-sup-0001-misc_information.pd

A Bi2Te3-Filled Nickel Foam Film with Exceptional Flexibility and Thermoelectric Performance

The past decades have witnessed surging demand for wearable electronics, for which thermoelectrics (TEs) are considered a promising self-charging technology, as they are capable of converting skin heat into electricity directly. Bi2Te3 is the most-used TE material at room temperature, due to a high zT of ~1. However, it is different to integrate Bi2Te3 for wearable TEs owing to its intrinsic rigidity. Bi2Te3 could be flexible when made thin enough, but this implies a small electrical and thermal load, thus severely restricting the power output. Herein, we developed a Bi2Te3/nickel foam (NiFoam) composite film through solvothermal deposition of Bi2Te3 nanoplates into porous NiFoam. Due to the mesh structure and ductility of Ni Foam, the film, with a thickness of 160 μm, exhibited a high figure of merit for flexibility, 0.016, connoting higher output. Moreover, the film also revealed a high tensile strength of 12.7 ± 0.04 MPa and a maximum elongation rate of 28.8%. In addition, due to the film’s high electrical conductivity and enhanced Seebeck coefficient, an outstanding power factor of 850 μW m−1 K−2 was achieved, which is among the highest ever reported. A module fabricated with five such n-type legs integrated electrically in series and thermally in parallel showed an output power of 22.8 nW at a temperature gap of 30 K. This work offered a cost-effective avenue for making highly flexible TE films for power supply of wearable electronics by intercalating TE nanoplates into porous and meshed-structure materials