Identifying design requirements for emerging markets

The manufacturing industry’s interest in emerging markets has been increasing dramatically during the recent decades as their economy is growing. Western companies are making efforts to develop products for emerging markets but are also facing various challenges in the process of doing so. One major challenge is the identification of reliable and valuable design requirements. This study aims at investigating the influence of the emerging market context on the practice of identifying design requirements. A survey among Danish industry was conducted with 130 responses collected. 92 answers provided an insight into design requirement identification in a western context, whereas 62 provided an insight into both emerging and western contexts. The results indicate the importance of design requirement identification when developing for emerging markets. Requirement elicitation and analysis are the most challenging phases in a design requirement identification process for both western and emerging markets. For Danish companies, identifying design requirements for emerging markets is more difficult than that for western markets, particularly when considering user needs, governmental regulations and organizational infrastructures

Simvastatin reduces high uric acid-induced oxidative stress and inflammatory response in vascular endothelial cells via nuclear factor E2-related factor 2 (Nrf2) signaling

Objective(s): Increased oxidative stress and inflammatory response are risk factors for kidney and cardiovascular diseases in patients with hyperuricemia. Uric acid (UA) has been reported to cause inflammation and oxidative damage in cells by inhibiting the nuclear factor E2-related factor 2 (Nrf2) pathway. Notably, Simvastatin (SIM) can regulate the Nrf2 pathway, but whether SIM can regulate inflammatory response and oxidative stress in vascular endothelial cells induced by high UA via this pathway has not been clarified.Materials and Methods: To demonstrate this speculation, cell activity, as well as apoptosis, was estimated employing CCK-8 and TUNEL, respectively. Indicators of oxidative stress and inflammation were assessed by related kits and western blotting. Subsequently, the effects of SIM on signaling pathways were examined using western blotting.Results: The result showed that after UA exposure, oxidative stress was activated and inflammation was increased, and SIM could reverse this trend. Meanwhile, SIM could inhibit high UA-induced apoptosis. In addition, western blotting results showed that SIM reversed the down-regulation of the expression of Nrf2 pathway-related proteins caused by high UA.Conclusion: SIM alleviated the inflammatory response as well as inhibiting oxidative stress through the Nrf2 pathway, thereby attenuating high UA-induced vascular endothelial cell injury

Deep Learning-empowered Predictive Precoder Design for OTFS Transmission in URLLC

To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient precoder design highly depends on the accurate instantaneous channel state information at the transmitter (ICSIT), which however, is not always available in practice. To overcome this problem, in this paper, we focus on the orthogonal time frequency space (OTFS)-based URLLC system and adopt a deep learning (DL) approach to directly predict the precoder for the next time frame to minimize the frame error rate (FER) via implicitly exploiting the features from estimated historical channels in the delay-Doppler domain. By doing this, we can guarantee the system reliability even without the knowledge of ICSIT. To this end, a general precoder design problem is formulated where a closed-form theoretical FER expression is specifically derived to characterize the system reliability. Then, a delay-Doppler domain channels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net) is proposed for predictive precoder design. In particular, both the convolutional neural network and LSTM modules are adopted in the proposed neural network to exploit the spatial-temporal features of wireless channels for improving the learning performance. Finally, simulation results demonstrated that the FER performance of the proposed method approaches that of the perfect ICSI-aided scheme.Comment: 8 pages, 6 figure

Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach

This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent reliability performance, pragmatic precoder design is an effective and indispensable solution. However, the design requires accurate instantaneous channel state information at the transmitter (ICSIT) which is not always available in practice. Motivated by this, we adopt a deep learning (DL) approach to exploit implicit features from estimated historical delay-Doppler domain channels (DDCs) to directly predict the precoder to be adopted in the next time frame for minimizing the frame error rate (FER), that can further improve the system reliability without the acquisition of ICSIT. To this end, we first establish a predictive transmission protocol and formulate a general problem for the precoder design where a closed-form theoretical FER expression is derived serving as the objective function to characterize the system reliability. Then, we propose a DL-based predictive precoder design framework which exploits an unsupervised learning mechanism to improve the practicability of the proposed scheme. As a realization of the proposed framework, we design a DDCs-aware convolutional long short-term memory (CLSTM) network for the precoder design, where both the convolutional neural network and LSTM modules are adopted to facilitate the spatial-temporal feature extraction from the estimated historical DDCs to further enhance the precoder performance. Simulation results demonstrate that the proposed scheme facilitates a flexible reliability-latency tradeoff and achieves an excellent FER performance that approaches the lower bound obtained by a genie-aided benchmark requiring perfect ICSI at both the transmitter and receiver.Comment: 31 pages, 12 figure

Comprehensive analysis of PSMD family members and validation of PSMD9 as a potential therapeutic target in human glioblastoma

Aims PSMD family members, as important components of the 26S proteasome, are well known to be involved in protein degradation. However, their role in glioblastoma (GBM) has not been rigorously investigated. We aimed to perform systematic analysis of the expression signature, prognostic significance and functions of PSMD family genes in GBM to reveal potential prognostic markers and new therapeutic targets among PSMD family members. Methods In this study, we systemically analyzed PSMD family members in terms of their expression profiles, prognostic implications, DNA methylation levels, and genetic alterations; the relationships between their expression levels and immune infiltration and drug sensitivity; and their potential functional enrichment in GBM through bioinformatics assessment. Moreover, in vitro and in vivo experiments were used to validate the biological functions of PSMD9 and its targeted therapeutic effect in GBM. Results The mRNA levels of PSMD5/8/9/10/11/13/14 were higher in GBM than in normal brain tissues, and the mRNA levels of PSMD1/4/5/8/9/11/12 were higher in high-grade glioma (WHO grade III & IV) than in low-grade glioma (WHO grade II). High mRNA expression of PSMD2/6/8/9/12/13/14 and low mRNA expression of PSMD7 were associated with poor overall survival (OS). Multivariate Cox regression analysis identified PSMD2/5/6/8/9/10/11/12 as independent prognostic factors for OS prediction. In addition, the protein–protein interaction network and gene set enrichment analysis results suggested that PSMD family members and their interacting molecules were involved in the regulation of the cell cycle, cell invasion and migration, and other biological processes in GBM. In addition, knockdown of PSMD9 inhibited cell proliferation, invasion and migration and induced G2/M cell cycle arrest in LN229 and A172 GBM cells. Moreover, PSMD9 promoted the malignant progression of GBM in vivo. GBM cell lines with high PSMD9 expression were more resistant to panobinostat, a potent deacetylase inhibitor, than those with low PSMD9 expression. In vitro and in vivo experiments further validated that PSMD9 overexpression rescued the GBM inhibitory effect of panobinostat. Conclusion This study provides new insights into the value of the PSMD family in human GBM diagnosis and prognosis evaluation, and we further identified PSMD9 as a potential therapeutic target. These findings may lead to the development of effective therapeutic strategies for GBM.publishedVersio