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61.
Simone Dinarelli Giovanni Longo Antonio Francioso Luciana Mosca Marco Girasole 《International journal of molecular sciences》2022,23(17)
Erythrocytes’ aging and mechano-transduction are fundamental cellular pathways that determine the red blood cells’ (RBCs) behavior and function. The aging pattern can be influenced, in morphological, biochemical, and metabolic terms by the environmental conditions. In this paper, we studied the effect of a moderate mechanical stimulation applied through external shaking during the RBCs aging and revealed a strong acceleration of the aging pattern induced by such stimulation. Moreover, we evaluated the behavior of the main cellular effectors and resources in the presence of drugs (diamide) or of specific inhibitors of the mechano-transduction (probenecid, carbenoxolone, and glibenclamide). This approach provided the first evidence of a direct cross-correlation between aging and mechano-transduction and permitted an evaluation of the overall metabolic regulation and of the insurgence of specific morphological features, such as micro-vesicles and roughness alterations. Overall, for the first time the present data provided a schematic to understand the integration of distinct complex patterns in a comprehensive view of the cell and of its interactions with the environment. Mechano-transduction produces structural effects that are correlated with the stimulation and the strength of the environmental stimulation is paramount to effectively activate and trigger the biological cascades initiated by the mechano-sensing. 相似文献
62.
Rajarajan Ganesan;Tarunpreet Kaur;Alisha Mittal;Mansi Sahi;Sushant Konar;Tanvir Samra;Goverdhan Dutt Puri;Shayam Kumar Singh Thingnum;Nitin Auluck; 《Computational Intelligence》2024,40(3):e12658
The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework. 相似文献
63.
The application of cross‐corpus for speech emotion recognition (SER) via domain adaptation methods have gain high acknowledgment for developing good robust emotion recognition systems using different corpora or datasets. However, the issue of cross‐lingual still remains a challenge in SER and needs more attention to resolve the scenario of applying different language types in both training and testing. In this paper, we propose a triple attentive asymmetric convolutional neural network to address the recognition of emotions for cross‐lingual and cross‐corpus speech in an unsupervised approach. The proposed method adopts the joint supervision of softmax loss and center loss to learn high power discriminative feature representations for target domain via the use of high quality pseudo‐labels. The proposed model uses three attentive convolutional neural networks asymmetrically, where two of the networks are used to artificially label unlabeled target samples as a result of their predictions from training on source labeled samples and the other network is used to obtain salient target discriminative features from the pseudo‐labeled target samples. We evaluate our proposed method on three different language types (i.e., English, German, and Italian) data sets. The experimental results indicate that, our proposed method achieves higher prediction accuracy over other state‐of‐the‐art methods. 相似文献
64.
Data driven-based intelligent fault diagnosis methods, as a promising approach, have been widely employed in the health management and maintenance decision of rotating machinery. However, the domain shift phenomenon caused by internal and external interference inevitably exists in practical application scenarios, which significantly deteriorates the performances of the intelligent diagnosis model. And the preparation of label information in real complex scenes is usually time-consuming and expensive. To overcome these challenges, a novel unsupervised domain adaptation framework named deep multi-scale adversarial network with attention (MSANA) is introduced for machinery fault diagnosis. It is established based on two main components, one is the shared feature generator, which is constructed by two novel multi-scale modules with attention mechanism, and the other part is a fault pattern recognition module composed of two differentiated discriminators. While the multi-scale module is used to obtain rich features through different internal perceptual scales, the attention mechanism determines the weights of different scales, which promotes the dynamic adjustment performance and adaptive ability of the model. Then, decision boundary assisted adversarial learning strategy is employed to eliminate domain distribution differences and obtain domain-invariant features. A total of ten rolling bearing-based transfer scenarios and six gearbox-based transfer scenarios are adopted to evaluate the transferability of the proposed MSANA model, and the cross-domain transfer results show that it has superior transferability and stability. 相似文献
65.
In this article, we propose a new criterion for a switching algorithm to be used in MIMO‐OFDM systems. The switching algorithm, which selects between spatial multiplexing and MIMO diversity using the Demmel condition number as selection metrics, is first analyzed. Then, we show the limitation of this criterion, when increasing the receive antenna number. As a solution, we propose multiantenna processing and SNR switching criterion as a novel selection metric. The channel between the transmitter and the receiver is assumed a Rayleigh‐fading channel. Simulations results show that the proposed switching algorithm improves the performances in terms of bit error rate (BER) for symmetric and asymmetric MIMO systems. The gain is about 3 dB at BER = 10−4 in (4×4) architecture and 2 dB at BER = 10−4 in (3×2) architecture. 相似文献
66.
目的 探索甲型肝炎病毒（Ｈ２株）细胞适应的分于机制。方法 将甲型肝炎减毒活疫苗毒株（ＨＡＶ Ｈ２Ｋ７）在人胚肺二倍体细胞ＫＭＢ１７上快速连续传代增强适应（１４ｄ）,检测连续传代后抗原滴度和感染性滴度,测定 第１８代（ＨＡＶ Ｈ２Ｋ７Ｐ１８）全基因组序列。结果 适应至第１８代抗原滴度达１：５１２,感染性滴度为７．５ＬｏｇＣＣＩＤ５０／ｍｌ。 整个基因组出现了１０个核苷酸突变,变异卒为０．１３％,变异较大区域位于５’末端非编码区（５’ＵＴＰ）,有３个核苷酸 变异。编码区７个交变,２个是无义突变,５个有义突变导致５个氨基酸变化,分别位于 ＶＰ２ Ａ－Ｓ;２Ｃ,Ｑ－Ｐ;３Ａ,Ｒ－ Ｃ;３Ｃ,Ｔ－Ａ和３Ｄ,Ｖ－Ｇ。结论 ＨＡＶ Ｈ２Ｋ７４因组５’ＵＴＲ、２Ｃ区、３Ａ区、３Ｃ区和３Ｄ区变异协同作用促进在 ＫＭＢ１７ 细胞上快速增强适应。 相似文献
67.
作为社会媒体文本情感分析的重要研究课题之一,跨领域文本情感分类旨在利用源领域资源或模型迁移地服务于目标领域的文本情感分类任务,其可以有效缓解目标领域中带标签数据不足问题.从3个角度对跨领域文本情感分类方法行了归纳总结:(1)按照目标领域中是否有带标签数据,可分为直推式和归纳式情感迁移方法;(2)按照不同情感适应性策略,可分为实例迁移方法、特征迁移方法、模型迁移方法、基于词典的方法、联合情感主题方法以及图模型方法等;(3)按照可用源领域个数,可分为单源和多源跨领域文本情感分类方法.此外,还介绍了深度迁移学习方法及其在跨领域文本情感分类的最新应用成果.最后,围绕跨领域文本情感分类面临的关键技术问题,对可能的突破方向进行了展望. 相似文献
68.
在深度学习的推动下,目标检测方法在近些年取得了很大的进展。但在特殊天气条件下,如在常见的雾霾、沙尘、雨天、雪天等天气条件下拍摄到的图像会退化模糊,难以提取有效特征,从而对后期的目标检测带来巨大的困难。对近年来国内外学者在特殊天气下目标检测的研究进行了总结归纳,从相关数据集的建立和用途、面向特殊天气条件下的图像恢复算法研究及其对目标检测任务的影响、迁移学习中的领域自适应方法在目标检测的应用三个方面进行分析。对相关方法的实验结果进行综合比较,并提出今后的研究重点。 相似文献
69.
70.
Downlink call admission control with power allocation and rate scheduling for IDMAbased multibeam satellite systems 
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下载免费PDF全文 Considering the advantage of interleave-division multiple-access (IDMA) technique and the technical bottlenecks in the existing satellite systems,IDMA is introduced into satellite communication network... 相似文献