Computer-supported cooperative work in tele home care : architecture design, implementation and evaluation

With the development of healthcare service and computer networks, the healthcare providers are focusing on how to implant new technologies into health sections. These instant messaging techniques can lead healthcare service more efficienct than before. It is necessary to design and construct a cooperative work platform for patients and healthcare providers. They are able to communicate with each other, share information or documents and access into health records. This project will concentrate on the system design, implementation and evaluation to achieve a better performance platform. The underlying data repository will encompass distributed system aspects and data warehousing approach to promote the functions of this healthcare system. This platform will be deployed to the tele home care service and in-depth study on the healthcare services. Some of the innovations will be included in system design. The target of this project is to design the architecture of collaborative workspace for healthcare personnel and implement a prototype with useful functions. The evaluation will be conducted to validate the efficiency of proposed distributed database for patient records. In addition, it will be more flexible and less data redundant. Meanwhile, a demo system will be deployed in order to show and define the implemented functions

Kruppel-Like Factor 4-Dependent Staufen1-Mediated mRNA Decay Regulates Cortical Neurogenesis

Kruppel-like factor 4 (Klf4) is a zinc-finger-containing protein that plays a critical role in diverse cellular physiology. While most of these functions attribute to its role as a transcription factor, it is postulated that Klf4 may play a role other than transcriptional regulation. Here we demonstrate that Klf4 loss in neural progenitor cells (NPCs) leads to increased neurogenesis and reduced self-renewal in mice. In addition, Klf4 interacts with RNA-binding protein Staufen1 (Stau1) and RNA helicase Ddx5/17. They function together as a complex to maintain NPC self-renewal. We report that Klf4 promotes Stau1 recruitment to the 3′-untranslated region of neurogenesis-associated mRNAs, increasing Stau1-mediated mRNA decay (SMD) of these transcripts. Stau1 depletion abrogated SMD of target mRNAs and rescued neurogenesis defects in Klf4-overexpressing NPCs. Furthermore, Ddx5/17 knockdown significantly blocked Klf4-mediated mRNA degradation. Our results highlight a novel molecular mechanism underlying stability of neurogenesis-associated mRNAs controlled by the Klf4/Ddx5/17/Stau1 axis during mammalian corticogenesis

Adaptive Collision Avoidance for Multiple UAVs in Urban Environments

The increasing number of unmanned aerial vehicles (UAVs) in low-altitude airspace is seriously threatening the safety of the urban environment. This paper proposes an adaptive collision avoidance method for multiple UAVs (mUAVs), aiming to provide a safe guidance for UAVs at risk of collision. The proposed method is formulated as a two−layer resolution framework with the considerations of speed adjustment and rerouting strategies. The first layer is established as a deep reinforcement learning (DRL) model with a continuous state space and action space that adaptively selects the most suitable resolution strategy for UAV pairs. The second layer is developed as a collaborative mUAV collision avoidance model, which combines a three-dimensional conflict detection and conflict resolution pool to perform resolution. To train the DRL model, in this paper, a deep deterministic policy gradient (DDPG) algorithm is introduced and improved upon. The results demonstrate that the average time required to calculate a strategy is 0.096 s, the success rate reaches 95.03%, and the extra flight distance is 26.8 m, which meets the real-time requirements and provides a reliable reference for human intervention. The proposed method can adapt to various scenarios, e.g., different numbers and positions of UAVs, with interference from random factors. The improved DDPG algorithm can also significantly improve convergence speed and save training time

Nomogram integrating gene expression signatures with clinicopathological features to predict survival in operable NSCLC: a pooled analysis of 2164 patients

Abstract Background The current tumor-node-metastasis (TNM) staging system is insufficient to predict outcome of patients with operable Non-Small Cell Lung Cancer (NSCLC) owing to its phenotypic and genomic heterogeneity. Integrating genomic signatures with clinicopathological factors may provide more detailed evaluation of prognosis. Methods All 2164 clinically annotated NSCLC samples (1326 in the training set and 838 in the validation set) with corresponding microarray data from 17 cohorts were pooled to develop and validate a clinicopathologic-genomic nomogram based on Cox regression model. Two computational methods were applied to these samples to capture expression pattern of genomic signatures representing biological statuses. Model performance was measured by the concordance index (C-index) and calibration plot. Risk group stratification was proposed for the nomogram. Results Multivariable analysis of the training set identified independent factors including age, TNM stage, combined prognostic classifier, non-overlapping signature, and the ratio of neutrophil to plasma cells. The C-index of the nomogram for predicting survival was statistically superior to that of the TNM stage (training set, 0.686 vs 0.627, respectively; P \u2009<\u2009.001; validation set, 0.689 vs 0.638, respectively; P \u2009<\u2009.001). The calibration plots showed that the predicted 1-, 3- and 5-year survival probabilities agreed well with the actual observations. Stratifying patients into three risk groups detected significant differences among survival curves. Conclusions These findings offer preliminary evidence that genomic data provide independent and complementary prognostic information and incorporation of this information can refine prognosis in NSCLC. Prospective studies are required to further explore the value of this composite model for prognostic stratification and tailored therapeutic strategies

Genetic Polymorphisms of the Telomerase Reverse Transcriptase Gene in Relation to Prostate Tumorigenesis, Aggressiveness and Mortality: A Cross-Ancestry Analysis

Background: Telomerase reverse transcriptase (TERT) has been consistently associated with prostate cancer (PCa) risk. However, few studies have explored the association between TERT variants and PCa aggressiveness. Methods: Individual and genetic data were obtained from UK Biobank and a Chinese PCa cohort (Chinese Consortium for Prostate Cancer Genetics). Results: A total of 209,694 Europeans (14,550 PCa cases/195,144 controls) and 8873 Chinese (4438 cases/4435 controls) were involved. Nineteen susceptibility loci with five novel ones (rs144704378, rs35311994, rs34194491, rs144020096, and rs7710703) were detected in Europeans, whereas seven loci with two novel ones (rs7710703 and rs11291391) were discovered in the Chinese cohort. The index SNP for the two ancestries was rs2242652 (odds ratio [OR] = 1.16, 95% confidence interval [CI]:1.12–1.20, p = 4.12 × 10−16) and rs11291391 (OR = 1.73, 95%CI:1.34–2.25, p = 3.04 × 10−5), respectively. SNPs rs2736100 (OR = 1.49, 95%CI:1.31–1.71, p = 2.91 × 10−9) and rs2853677 (OR = 1.74, 95%CI:1.52–1.98, p = 3.52 × 10−16) were found significantly associated with aggressive PCa, while rs35812074 was marginally related to PCa death (hazard ratio [HR] = 1.61, 95%CI:1.04–2.49, p = 0.034). Gene-based analysis showed a significant association of TERT with PCa (European: p = 3.66 × 10−15, Chinese: p = 0.043) and PCa severity (p = 0.006) but not with PCa death (p = 0.171). Conclusion: TERT polymorphisms were associated with prostate tumorigenesis and severity, and the genetic architectures of PCa susceptibility loci were heterogeneous among distinct ancestries