Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.Comment: 6 page

Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal

In computational tongue diagnosis, specular reflection is generally inevitable in tongue image acquisition, which has adverse impact on the feature extraction and tends to degrade the diagnosis performance. In this paper, we proposed a two-stage (i.e., the detection and inpainting pipeline) approach to address this issue: (i) by considering both highlight reflection and subreflection areas, a superpixel-based segmentation method was adopted for the detection of the specular reflection areas; (ii) by extending the weighted nuclear norm minimization (WNNM) model, a nonlocal inpainting method is proposed for specular reflection removal. Experimental results on synthetic and real images show that the proposed method is accurate in detecting the specular reflection areas and is effective in restoring tongue image with more natural texture information of tongue body

Study of the intense pulsed electron beam energy spectrum from BIPPAB-450

Intense pulsed particle beams have been widely used and studied as an effective method for material surface modification in the past several decades. Beihang Intense Pulsed PArticle Beams 450 accelerator (BIPPAB-450) can produce Intense Pulsed Ion Beams (IPIB) and Electron Beams (IPEB) in two modes with different Magnetically Insulated Diodes (MID). For IPEB, the pulse duration, accelerating voltage, total beam current are 100ns, up to 450keV and 3kA, respectively..

Ultrastructural insights into cellular organization, energy storage and ribosomal dynamics of an ammonia-oxidizing archaeon from oligotrophic oceans

IntroductionNitrososphaeria, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their importance in Earth’s ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant unanswered question of how the machinery of these organisms underpins metabolic functions.MethodsIn this study, we combined spherical-chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaeria.Results and DiscussionOur tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules: one type is possibly composed of polyphosphate and the other polyhydroxyalkanoate. With precise measurements using cryo-ET, we observed low quantity and density of ribosomes in SCM1 cells, which are in alignment with the documented slow growth of AOA in laboratory cultures. Collectively, these findings provide visual evidence supporting the resilience of AOA in the vast oligotrophic marine environment

Human Absorbable MicroRNA Prediction based on an Ensemble Manifold Ranking Model

MicroRNAs, a class of short non-coding RNAs, are able to regulate more than half of human genes and affect many fundamental biological processes. It has been long considered synthesized endogenously until very recent discoveries showing that human can absorb exogenous microRNAs from dietary resources. This finding has raised a challenge scientific question: which exogenous microRNAs can be integrated into human circulation and possibly exert functions in human? Here we present a well-designed ensemble manifold ranking model for identifying human absorbable exogenous miRNAs from 14 common dietary species. Specifically, we have analyzed 4,910 dietary microRNAs with 1,120 features derived based on the microRNA sequence and structure. In total, 70 discriminative features were selected to characterize the circulating microRNAs in human and have been used to infer the possibility of a certain exogenous microRNA getting integrated into human circulation. Finally, 461 dietary microRNAs have been identified as transportable exogenous microRNAs. To assess the performance of our ensemble model, we have validated the top predictions through a milk-feeding study. In addition, 26 microRNAs from two virus species were predicted as transportable and have been validated in two external experiments. The results demonstrate the data-driven computational model is highly promising to study transportable microRNAs while bypassing the complex mechanistic details

Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

Improving the accuracy of wind power forecasting can guarantee the stable dispatch and safe operation of the grid system. Here, we propose an EMD-PCA-RF-LSTM wind power forecasting model to solve problems in traditional wind power forecasting such as incomplete consideration of influencing factors, inaccurate feature identification, and complex space–time relationships between variables. The proposed model incorporates Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), Random Forest (RF), and Long Short-Term Memory (LSTM) neural networks, And environmental factors are filtered by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm when pre-processing the data. First, the environmental factors are extended by the EMD algorithm to reduce the non-stationarity of the series. Second, the key influence series are extracted by the PCA algorithm in order to remove noisy information, which can seriously interfere with the data regression analysis. The data are then subjected to further feature extraction by calculating feature importance through the RF algorithm. Finally, the LSTM algorithm is used to perform dynamic time modeling of multivariate feature series for wind power forecasting. The above combined model is beneficial for analyzing the effects of different environmental factors on wind power and for obtaining more accurate prediction results. In a case study, the proposed combined forecasting model was verified using actual measured data from a power station. The results indicate that the proposed model provides the most accurate results when compared to benchmark models: MSE 7.26711 MW, RMSE 2.69576 MW, MAE 1.73981 MW, and adj-R2 0.9699203s

Postoperative survival of pulmonary invasive mucinous adenocarcinoma versus non-mucinous invasive adenocarcinoma

Abstract Purpose In 2015, the World Health Organization renamed mucinous bronchioloalveolar adenocarcinoma as pulmonary invasive mucinous adenocarcinoma (IMA). Due to its low incidence and unclear prognosis with surgical treatment, previous studies have presented opposing survival outcomes. We aimed to investigate the differences in surgical prognosis and prognosis-related risk factors by comparing IMA with non-mucinous invasive adenocarcinoma (NMA). Methods A total of 20,914 patients diagnosed with IMA or NMA from 2000 to 2014 were screened from the Surveillance, Epidemiology, and End Results database. The screened patients were subjected to propensity score matching (PSM) in a 1:4 ratio to explore the survival differences between patients with IMA and NMA and the factors influencing prognosis. Results For all patients, IMA was prevalent in the lower lobes of the lungs (p < 0.0001), well-differentiated histologically (p < 0.0001), less likely to have lymph node metastases (94.4% vs. 72.0%, p < 0.0001) and at an earlier pathological stage (p = 0.0001). After PSM, the IMA cohort consisted of 303 patients, and the NMA cohort consisted of 1212 patients. Kaplan‒Meier survival analysis showed no difference in overall survival (OS) between patients in the IMA cohort and those in the NMA cohort (p = 0.7). Cox proportional hazards analysis showed that differences in tumor pathological type did not influence OS between the two cohorts (p = 0.65). Age (HR: 1.98, 95% CI 1.7–2.31, p < 0.0001), gender (HR: 0.64, 95% CI 0.55–0.75, p < 0.0001), and radiation treatment (HR: 2.49, 95% CI 1.84–3.37, p < 0.0001) were independent predictors of patient OS. Conclusion There was no significant difference in OS between patients with IMA and those with NMA after surgical treatment. Age, sex, and radiation treatment can independently predict OS

Identification and Analysis of Sex-Biased Copy Number Alterations

Background: Sex difference has long been recognized at cancer incidence, outcomes, and responses to therapy. Analyzing the somatic mutation profiles of large-scale cancer samples between the sexes have revealed several potential drivers of cancer with sex difference. However, it is still a demand for in-depth scrutinizing the sex-biased characteristics of genome instability to link the clinical differences for individual cancer type. Methods: Here, we utilized a published framework devised to specifically compare the copy number profiles between 2 groups to identify the sex-biased copy number alterations (CNAs) across 16 cancer types from the The Cancer Genome Atlas Program database, and dissected the impact of those CNAs. Results: Totally, 81 male-biased CNA regions and 23 female-biased CNA regions in 16 cancer types were found. Functional annotation analysis showed that several critical biological functions associated with sex-biased CNAs are shared in multiple cancer types, including immune-related pathways and regulation of cellular signaling. Most sex-biased CNAs have a substantial effect on transcriptional consequence, where the average of over 68% of genes have a linear relationship with CNAs across cancer types, and 14% of those genes are affected by the combination of the sex and copy number. Furthermore, 29 sex-biased CNA regions show latent capacity to be sex-specific prognostic biomarker such as CNA on 11q13.4 for head and neck cancer and lung cancer. Conclusions: This analysis offers new insights into the role of sex in cancer etiology and prognosis through a detailed characterization of sex differences in genome instability of diverse cancers