Establishing and characterizing human stem cells from the apical papilla immortalized by hTERT gene transfer

Stem cells from the apical papilla (SCAPs) are promising candidates for regenerative endodontic treatment and tissue regeneration in general. However, harvesting enough cells from the limited apical papilla tissue is difficult, and the cells lose their primary phenotype over many passages. To get over these challenges, we immortalized human SCAPs with lentiviruses overexpressing human telomerase reverse transcriptase (hTERT). Human immortalized SCAPs (hiSCAPs) exhibited long-term proliferative activity without tumorigenic potential. Cells also expressed mesenchymal and progenitor biomarkers and exhibited multiple differentiation potentials. Interestingly, hiSCAPs gained a stronger potential for osteogenic differentiation than the primary cells. To further investigate whether hiSCAPs could become prospective seed cells in bone tissue engineering, in vitro and in vivo studies were performed, and the results indicated that hiSCAPs exhibited strong osteogenic differentiation ability after infection with recombinant adenoviruses expressing BMP9 (AdBMP9). In addition, we revealed that BMP9 could upregulate ALK1 and BMPRII, leading to an increase in phosphorylated Smad1 to induce the osteogenic differentiation of hiSCAPs. These results support the application of hiSCAPs in tissue engineering/regeneration schemes as a stable stem cell source for osteogenic differentiation and biomineralization, which could be further used in stem cell-based clinical therapies

A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level. © 2019, Springer Science+Business Media, LLC, part of Springer Nature

Effects of magnanimous therapy on emotional, psychosomatic and immune functions of lung cancer patients

This study was a randomised controlled study on the effects of the individual computer magnanimous therapy and group computer magnanimous therapy on emotional, psychosomatic and immune function among advanced lung cancer patients. Patients were examined at baseline and 2 weeks later using the Psychosomatic Status Scale for Cancer Patients, Hospital Anxiety Depression Scale and IgA, IgG, IgM and natural killer cell functions. The results showed that individual computer magnanimous therapy and group computer magnanimous therapy were beneficial for advanced lung cancer patients in improving depression, anxiety, psychosomatic status and immune functions. The improvements of immune functions may be related to the improvements of the participants’ emotional and psychosocial status

Why not glycine electrochemical biosensors?

Glycine monitoring is gaining importance as a biomarker in clinical analysis due to its involvement in multiple physiological functions, which results in glycine being one of the most analyzed biomolecules for diagnostics. This growing demand requires faster and more reliable, while affordable, analytical methods that can replace the current gold standard for glycine detection, which is based on sample extraction with subsequent use of liquid chromatography or fluorometric kits for its quantification in centralized laboratories. This work discusses electrochemical sensors and biosensors as an alternative option, focusing on their potential application for glycine determination in blood, urine, and cerebrospinal fluid, the three most widely used matrices for glycine analysis with clinical meaning. For electrochemical sensors, voltammetry/amperometry is the preferred readout (10 of the 13 papers collected in this review) and metal-based redox mediator modification is the predominant approach for electrode fabrication (11 of the 13 papers). However, none of the reported electrochemical sensors fulfill the requirements for direct analysis of biological fluids, most of them lacking appropriate selectivity, linear range of response, and/or capability of measuring at physiological conditions. Enhanced selectivity has been recently reported using biosensors (with an enzyme element in the electrode design), although this is still a very incipient approach. Currently, despite the benefits of electrochemistry, only optical biosensors have been successfully reported for glycine detection and, from all the inspected works, it is clear that bioengineering efforts will play a key role in the embellishment of selectivity and storage stability of the sensing element in the sensor

A proactive mobile edge cache policy based on the prediction by partial matching

The proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering the prediction of the content popularity and user location to minimise the latency and maximise the cache hit rate. Here, a backpropagation neural network is applied to predict the content popularity, and prediction by partial matching is chosen to predict the user location. The simulation results reveal our proposed cache policy is around 27%-60% improved in the cache hit ratio and 14%-60% reduced in the average latency, compared with the two conventional reactive policies, i.e., LFU and LRU policies

Fine-Grained Scene Graph Generation with Data Transfer

Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images. Recent works have made a steady progress on SGG, and provide useful tools for high-level vision and language understanding. However, due to the data distribution problems including long-tail distribution and semantic ambiguity, the predictions of current SGG models tend to collapse to several frequent but uninformative predicates (e.g., on, at), which limits practical application of these models in downstream tasks. To deal with the problems above, we propose a novel Internal and External Data Transfer (IETrans) method, which can be applied in a plug-and-play fashion and expanded to large SGG with 1,807 predicate classes. Our IETrans tries to relieve the data distribution problem by automatically creating an enhanced dataset that provides more sufficient and coherent annotations for all predicates. By training on the enhanced dataset, a Neural Motif model doubles the macro performance while maintaining competitive micro performance. The code and data are publicly available at https://github.com/waxnkw/IETrans-SGG.pytorch.Comment: ECCV 2022 (Oral

A smart cache content update policy based on deep reinforcement learning

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%-64% improved in terms of the cache hit ratio and 56%-59% decreased in terms of the average latency