Study of the cytological features of bone marrow mesenchymal stem cells from patients with neuromyelitis optica.

Neuromyelitis optica (NMO) is a refractory autoimmune inflammatory disease of the central nervous system without an effective cure. Autologous bone marrow‑derived mesenchymal stem cells (BM‑MSCs) are considered to be promising therapeutic agents for this disease due to their potential regenerative, immune regulatory and neurotrophic effects. However, little is known about the cytological features of BM‑MSCs from patients with NMO, which may influence any therapeutic effects. The present study aimed to compare the proliferation, differentiation and senescence of BM‑MSCs from patients with NMO with that of age‑ and sex‑matched healthy subjects. It was revealed that there were no significant differences in terms of cell morphology or differentiation capacities in the BM‑MSCs from the patients with NMO. However, in comparison with healthy controls, BM‑MSCs derived from the Patients with NMO exhibited a decreased proliferation rate, in addition to a decreased expression of several cell cycle‑promoting and proliferation‑associated genes. Furthermore, the cell death rate increased in BM‑MSCs from patients under normal culture conditions and an assessment of the gene expression profile further confirmed that the BM‑MSCs from patients with NMO were more vulnerable to senescence. Platelet‑derived growth factor (PDGF), as a major mitotic stimulatory factor for MSCs and a potent therapeutic cytokine in demyelinating disease, was able to overcome the decreased proliferation rate and increased senescence defects in BM‑MSCs from the patients with NMO. Taken together, the results from the present study have enabled the proposition of the possibility of combining the application of autologous BM‑MSCs and PDGF for refractory and severe patients with NMO in order to elicit improved therapeutic effects, or, at the least, to include PDGF as a necessary and standard growth factor in the current in vitro formula for the culture of NMO patient‑derived BM‑MSCs

A serial dual-channel library occupancy detection system based on Faster RCNN

The phenomenon of seat occupancy in university libraries is a prevalent issue. However, existing solutions, such as software-based seat reservations and sensors-based occupancy detection, have proven to be inadequate in effectively addressing this problem. In this study, we propose a novel approach: a serial dual-channel object detection model based on Faster RCNN. Furthermore, we develop a user-friendly Web interface and mobile APP to create a computer vision-based platform for library seat occupancy detection. To construct our dataset, we combine real-world data collec-tion with UE5 virtual reality. The results of our tests also demonstrate that the utilization of per-sonalized virtual dataset significantly enhances the performance of the convolutional neural net-work (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three es-sential steps. Firstly, we employ Faster RCNN algorithm to determine whether a seat is occupied by an individual. Subsequently, we utilize an object classification algorithm based on transfer learning, to classify and identify images of unoccupied seats. This eliminates the need for manual judgment regarding whether a person is suspected of occupying a seat. Lastly, the Web interface and APP provide seat information to librarians and students respectively, enabling comprehensive services. By leveraging deep learning methodologies, this research effectively addresses the issue of seat occupancy in library systems. It significantly enhances the accuracy of seat occupancy recognition, reduces the computational resources required for training CNNs, and greatly improves the effi-ciency of library seat management

SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition

Distantly-Supervised Named Entity Recognition effectively alleviates the burden of time-consuming and expensive annotation in the supervised setting. But the context-free matching process and the limited coverage of knowledge bases introduce inaccurate and incomplete annotation noise respectively. Previous studies either considered only incomplete annotation noise or indiscriminately handle two types of noise with the same strategy. In this paper, we argue that the different causes of two types of noise bring up the requirement of different strategies in model architecture. Therefore, we propose the SANTA to handle these two types of noise separately with (1) Memory-smoothed Focal Loss and Entity-aware KNN to relieve the entity ambiguity problem caused by inaccurate annotation, and (2) Boundary Mixup to alleviate decision boundary shifting problem caused by incomplete annotation and a noise-tolerant loss to improve the robustness. Benefiting from our separate tailored strategies, we confirm in the experiment that the two types of noise are well mitigated. SANTA also achieves a new state-of-the-art on five public datasets.Comment: Findings of ACL202

Characteristics of Pollen from Transgenic Lines of Apple Carrying the Exogenous CpTI Gene

AbstractIt is fundamental for gene transformation and ecosystem hazard evaluation to study the pollen characteristics of transgenic plants. In this research, the characteristics of pollen from 7- or 8-year-old transgenic apple plants carrying an exogenous CpTI gene were analyzed. The results showed that there was no significant difference in terms of size, morphology, or exine ornamentation between the pollen of the transgenic plants and the non-transgenic control. However, the transgenic plants had more abnormal pollen grains. Of the 13 transgenic lines tested, 12 had a significantly lower amount of pollen and six exhibited a significantly lower germination rate when cultured in vitro. The pollen viability of three transgenic lines was determined, with two showing significantly lower viability than the control. The transgenic Gala apple pollen grains germinated normally via controlled pollination on Fuji apple stigmas. However, the pollen tubes extended relatively slowly during the middle and late development stages, and another 8h were needed to reach the ovules compared with the control. The gibberellic acid concentration in transgenic Gala apple flowers was lower than in the non-transgenic control during all development stages tested. The abscisic acid concentration in the transgenic flowers was lower during the pink stage, and higher during the ball and fully open stages. Microscopic observation of the anther structure showed no difference. The tapetum of the pollen sac wall in transgenic plants decomposed late and affected pollen grain development, which could be one of the reasons for the lower number of pollen grains and poor viability in the transgenic plants

Activity of Platinum/Carbon and Palladium/Carbon Catalysts Promoted by Ni2P in Direct Ethanol Fuel Cells

Ethanol is an alternative fuel for direct alcohol fuel cells, in which the electrode materials are commonly based on Pt or Pd. Owing to the excellent promotion effect of Ni2P that was found in methanol oxidation, we extended the catalyst system of Pt or Pd modified by Ni2P in direct ethanol fuel cells. The Ni2P-promoted catalysts were compared to commercial catalysts as well as to reference catalysts promoted with only Ni or only P. Among the studied catalysts, Pt/C and Pd/C modified by Ni2P (30 wt%) showed both the highest activity and stability. Upon integration into the anode of a homemade direct ethanol fuel cell, the Pt-Ni2P/C-30% catalyst showed a maximum power density of 21 mWcm^-2, which is approximately two times higher than that of a commercial Pt/C catalyst. The Pd- Ni2P/C-30% catalyst exhibited a maximum power density of 90 mWcm^-2. This is approximately 1.5 times higher than that of a commercial Pd/C catalyst. The discharge stability on both two catalysts was also greatly improved over a 12 h discharge operation

Can Language Models Pretend Solvers? Logic Code Simulation with LLMs

Transformer-based large language models (LLMs) have demonstrated significant potential in addressing logic problems. capitalizing on the great capabilities of LLMs for code-related activities, several frameworks leveraging logical solvers for logic reasoning have been proposed recently. While existing research predominantly focuses on viewing LLMs as natural language logic solvers or translators, their roles as logic code interpreters and executors have received limited attention. This study delves into a novel aspect, namely logic code simulation, which forces LLMs to emulate logical solvers in predicting the results of logical programs. To further investigate this novel task, we formulate our three research questions: Can LLMs efficiently simulate the outputs of logic codes? What strength arises along with logic code simulation? And what pitfalls? To address these inquiries, we curate three novel datasets tailored for the logic code simulation task and undertake thorough experiments to establish the baseline performance of LLMs in code simulation. Subsequently, we introduce a pioneering LLM-based code simulation technique, Dual Chains of Logic (DCoL). This technique advocates a dual-path thinking approach for LLMs, which has demonstrated state-of-the-art performance compared to other LLM prompt strategies, achieving a notable improvement in accuracy by 7.06% with GPT-4-Turbo.Comment: 12 pages, 8 figure

Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection

Millimeter-wave (MMW) imaging is emerging as a promising technique for safe security inspection. It achieves a delicate balance between imaging resolution, penetrability and human safety, resulting in higher resolution compared to low-frequency microwave, stronger penetrability compared to visible light, and stronger safety compared to X ray. Despite of recent advance in the last decades, the high cost of requisite large-scale antenna array hinders widespread adoption of MMW imaging in practice. To tackle this challenge, we report a large-scale single-shot MMW imaging framework using sparse antenna array, achieving low-cost but high-fidelity security inspection under an interpretable learning scheme. We first collected extensive full-sampled MMW echoes to study the statistical ranking of each element in the large-scale array. These elements are then sampled based on the ranking, building the experimentally optimal sparse sampling strategy that reduces the cost of antenna array by up to one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, which realizes robust and accurate image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at the same sample sampling ratio. The performance of the reported technique presents higher than 50% superiority over the existing MMW imaging schemes on various metrics including precision, recall, and mAP50. With such strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging, and could advocate its further practical applications