Wi-Fi Teeter-Totter: Overclocking OFDM for Internet of Things

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce the IoT's TX power by boosting the decoding capability at the receivers

E2F1 Suppresses Oxidative Metabolism and Endothelial Differentiation of Bone Marrow Progenitor Cells

RATIONALE: The majority of current cardiovascular cell therapy trials use bone marrow progenitor cells (BM PCs) and achieve only modest efficacy; the limited potential of these cells to differentiate into endothelial-lineage cells is one of the major barriers to the success of this promising therapy. We have previously reported that the E2F transcription factor 1 (E2F1) is a repressor of revascularization after ischemic injury. OBJECTIVE: We sought to define the role of E2F1 in the regulation of BM PC function. METHODS AND RESULTS: Ablation of E2F1 (E2F1 deficient) in mouse BM PCs increases oxidative metabolism and reduces lactate production, resulting in enhanced endothelial differentiation. The metabolic switch in E2F1-deficient BM PCs is mediated by a reduction in the expression of pyruvate dehydrogenase kinase 4 and pyruvate dehydrogenase kinase 2; overexpression of pyruvate dehydrogenase kinase 4 reverses the enhancement of oxidative metabolism and endothelial differentiation. Deletion of E2F1 in the BM increases the amount of PC-derived endothelial cells in the ischemic myocardium, enhances vascular growth, reduces infarct size, and improves cardiac function after myocardial infarction. CONCLUSION: Our results suggest a novel mechanism by which E2F1 mediates the metabolic control of BM PC differentiation, and strategies that inhibit E2F1 or enhance oxidative metabolism in BM PCs may improve the effectiveness of cell therapy

IQP TS2 Machine Learning in Cancer Detection

The use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. Algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The research trains algorithms on data from the evaluation sample before they are used to predict the diagnostic outcome in the validation dataset, and compares the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. The research explores the use of averaging and voting ensembles to improve predictive performance and provides a step-by-step guide to developing algorithms using the open-source programming environment. This research uses a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be applied to other complex tasks including natural language processing and image recognition

Superior Clone Selection in a <i>Eucalyptus</i> Trial Using Forest Phenotyping Technology via UAV-Based DAP Point Clouds and Multispectral Images

The quantitative, accurate and efficient acquisition of tree phenotypes is the basis for forest “gene-phenotype-environment” studies. It also offers significant support for clarifying the genetic control mechanisms of tree traits. The application of unmanned aerial vehicle (UAV) remote sensing technology to the collection of phenotypic traits at an individual tree level quantitatively analyses tree phenology and directionally evaluates tree growth, as well as accelerating the process of forest genetics and breeding. In this study, with the help of high-resolution, high-overlap, multispectral images obtained by an UAV, combined with digital elevation models (DEMs) extracted from point clouds acquired by a backpack LiDAR, a high-throughput tree structure and spectral phenotypic traits extraction and a genetic selection were conducted in a trial of Eucalyptus clones in the State-owned Dongmen Forest Farm in the Guangxi Zhuang Autonomous Region. Firstly, we validated the accuracy of extracting the phenotypic parameters of individual tree growth based on aerial stereo photogrammetry point clouds. Secondly, on this basis, the repeatability of the tree growth traits and vegetation indices (VIs), the genetic correlation coefficients between the traits were calculated. Finally, the eucalypt clones were ranked by integrating a selection index of traits, and the superior genotypes were selected and their genetic gain predicted. The results showed a high accuracy of the tree height (H) extracted from the digital aerial photogrammetry (DAP) point cloud based on UAV images (R2 = 0.91, and RMSE = 0.56 m), and the accuracy of estimating the diameter at breast height (DBH) was R2 = 0.71, and RMSE = 0.75 cm. All the extracted traits were significantly different within the tree species and among the clones. Except for the crown width (CW), the clonal repeatability (Rc) of the traits were all above 0.9, and the individual repeatability values (Ri) were all above 0.5. The genetic correlation coefficient between the tree growth traits and VIs fluctuated from 0.3 to 0.5, while the best clones were EA14-15, EA14-09, EC184, and EC183 when the selection proportion was 10%. The purpose of this study was to construct a technical framework for phenotypic traits extraction and genetic analysis of trees based on unmanned aerial stereo photography point clouds and high-resolution multispectral images, while also exploring the application potential of this approach in the selective breeding of eucalypt clones

Reliable and Efficient Agrobacterium tumefaciens-Mediated Genetic Transformation of Dianthus spiculifolius

Dianthus spiculifolius is a perennial herbaceous flower with strong environmental adaptability and is an important ornamental ground cover plant. In this study, seeds of D. spiculifolius were used as explants for callus induction, adventitious bud differentiation, and rooting by adding different concentrations of 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzyl aminopurine (6-BA), and naphthaleneacetic acid (NAA) to Murashige and Skoog medium. The calli generated were co-cultured with Agrobacterium tumefaciens EHA105 containing pBI121-GUS or pBI121-GFP plasmids for 30 min, and transgenic regenerated plants were obtained by kanamycin (30 mg · L−1) screening. RT-PCR confirmed the stable expression of the exogenous GUS and GFP genes in the D. spiculifolius. The β-glucuronidase (GUS) histochemical staining confirmed GUS gene expression in transgenic calli, adventitious buds, and regenerated plants of D. spiculifolius. The green fluorescent protein (GFP) visual analysis showed GFP gene expression in transgenic calli. Furthermore, subcellular localization analysis showed that the three organelle marker proteins were not only successfully expressed but also accurately localized to their corresponding organelles in D. spiculifolius callus cells. These results indicated a successful establishment of a reliable and efficient A. tumefaciens-mediated genetic transformation system, which will contribute to functional gene research and genetic improvement of D. spiculifolius

Minority stress, depression, and cigarette smoking among Chinese gay versus bisexual men: a two-group structural equation model analyses.

Abstract Background Literature in the West suggested that bisexual men have a higher smoking rate compared to gay men. Data on patterns of smoking among gay and bisexual men are limited in Eastern Asian countries like China. This study examined the cigarette smoking prevalence for gay versus bisexual men in China and their unique minority stress - smoking pathways. Methods Between September 2017 and November 2018, we surveyed a convenience sample of 538 gay men and 138 bisexual men recruited from local sexual minority organizations in four metropolitan cities in China (i.e., Beijing, Wuhan, Nanchang, and Changsha). Measures included sexual orientation, sociodemographics, theory-based minority stressors, depressive symptoms, and past 30-day cigarette smoking. Two-group (gay men vs. bisexual men) structural equation modeling (SEM) was used to test possible distinct mechanisms between theory-based stressors, depressive symptoms, and cigarette smoking among gay men and bisexual men, respectively. Results The mean age of participants was 26.51 (SD = 8.41) years old and 76.3% of them had at least a college degree. Bisexual men reported a higher rate of cigarette smoking compared to gay men (39.9% vs. 27.3%). Two-group SEM indicated that the pathways for cigarette smoking were not different between gay and bisexual men. Higher rejection anticipation was associated with greater depressive symptoms (standardized β = 0.32, p < .001), and depressive symptoms were not associated with cigarette smoking. Conclusions Minority stress, specifically rejection anticipation, may be critical considerations in addressing depressive symptoms, but not smoking, among both gay and bisexual men in China