MiR-646 targets PDK1 to recede aerobic glycolysis and cell proliferation in nasopharyngeal carcinoma

Purpose: To investigate the effect and mechanism of miR-646 on aerobic glycolysis and cell proliferation in nasopharyngeal carcinoma. Methods: MiR-646 expression in human nasopharyngeal carcinoma cell lines was determined by quantitative real-time polymerase chain reaction) (qRT-PCR). Cell counting kit-8 (CCK8) was used to evaluate cell viability, and colony formation assay was also performed. The target of miR-646 was determined by luciferase activity assay. The effect of miR-646 on aerobic glycolysis was assessed via glucose uptake, and lactate and ATP production. Western blot analysis was conducted to unravel the underlying mechanism involved in the regulation of miR-646 in nasopharyngeal carcinoma. Results: MiR-646 was downregulated in human nasopharyngeal carcinoma cell lines. MiR-646 mimics decreased cell viability and inhibited cell proliferation, whereas miR-646 inhibitor increased cell viability and promoted cell proliferation. Pyruvate dehydrogenase kinase 1(PDK1) was identified as a target of miR-646, and its expression was negatively regulated by miR-646. MiR-646 probably inhibited aerobic glycolysis via regulation of PDK1, as shown by decreased glucose uptake and decreased lactate and ATP production. The inhibitory effect of miR-646 on nasopharyngeal carcinoma cell proliferation was partly via PDK1 regulation. Conclusion: MiR-646 inhibits aerobic glycolysis in nasopharyngeal carcinoma and promotes cell proliferation via suppression of PDK1, suggesting miR-646 as a potential therapeutic target in nasopharyngeal carcinoma

Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

We investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise. To better distinguish these noise types and utilize their semantics, we propose a novel sample selection-based approach for noisy label learning, called Proto-semi. Proto-semi initially divides all samples into the confident and unconfident datasets via warm-up. By leveraging the confident dataset, prototype vectors are constructed to capture class characteristics. Subsequently, the distances between the unconfident samples and the prototype vectors are calculated to facilitate noise classification. Based on these distances, the labels are either corrected or retained, resulting in the refinement of the confident and unconfident datasets. Finally, we introduce a semi-supervised learning method to enhance training. Empirical evaluations on a real-world annotated dataset substantiate the robustness of Proto-semi in handling the problem of learning from noisy labels. Meanwhile, the prototype-based repartitioning strategy is shown to be effective in mitigating the adverse impact of label noise. Our code and data are available at https://github.com/fuxiAIlab/ProtoSemi

Towards Long-term Annotators: A Supervised Label Aggregation Baseline

Relying on crowdsourced workers, data crowdsourcing platforms are able to efficiently provide vast amounts of labeled data. Due to the variability in the annotation quality of crowd workers, modern techniques resort to redundant annotations and subsequent label aggregation to infer true labels. However, these methods require model updating during the inference, posing challenges in real-world implementation. Meanwhile, in recent years, many data labeling tasks have begun to require skilled and experienced annotators, leading to an increasing demand for long-term annotators. These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works. Hereby, in this paper, we propose a novel label aggregation technique, which does not need any model updating during inference and can extensively explore the historical annotation records. We call it SuperLA, a Supervised Label Aggregation method. Inside this model, we design three types of input features and a straightforward neural network structure to merge all the information together and subsequently produce aggregated labels. Based on comparison experiments conducted on 22 public datasets and 11 baseline methods, we find that SuperLA not only outperforms all those baselines in inference performance but also offers significant advantages in terms of efficiency

Fog Orchestration and Simulation for IoT Services

The Internet of Things (IoT) interconnects physical objects including sensors, vehicles, and buildings into a virtual circumstance, resulting in the increasing integration of Cyber-physical objects. The Fog computing paradigm extends both computation and storage services in Cloud computing environment to the network edge. Typically, IoT services comprise of a set of software components running over different locations connected through datacenter or wireless sensor networks. It is significantly important and cost-effective to orchestrate and deploy a group of microservices onto Fog appliances such as edge devices or Cloud servers for the formation of such IoT services. In this chapter, we discuss the challenges of realizing Fog orchestration for IoT services, and present a software-defined orchestration architecture and simulation solutions to intelligently compose and orchestrate thousands of heterogeneous Fog appliances. The resource provisioning, component placement and runtime QoS control in the orchestration procedure can harness workload dynamicity, network uncertainty and security demands whilst considering different applications’ requirement and appliances’ capabilities. Our practical experiences show that the proposed parallelized orchestrator can reduce the execution time by 50% with at least 30% higher orchestration quality. We believe that our solution plays an important role in the current Fog ecosystem

Abelian and non-abelian quantum two-block codes

We discuss quantum two-block codes, a large class of CSS codes constructed from two commuting square matrices.Interesting families of such codes are generalized-bicycle (GB) codes and two-block group-algebra (2BGA) codes, where a cyclic group is replaced with an arbitrary finite group, generally non-abelian. We present code construction and give several expressions for code dimension, applicable depending on whether the constituent group is cyclic, abelian, or non-abelian. This gives a simple criterion for an essentially non-abelian 2BGA code guaranteed not to be permutation-equivalent to such a code based on an abelian group. We also give a lower bound on the distance which, in particular, applies to the case when a 2BGA code reduces to a hypergraph-product code constructed from a pair of classical group codes

Infrared Radiation of Graphene Electrothermal Film Triggered Alpha and Theta Brainwaves

The alpha and theta frequency brainwave activity in Electroencephalogram (EEG) signal has been correlated with attention, inhibitory processes, memory, perceptual abilities, and sleep. The enhanced alpha and theta brainwave activity may bring positive behavioral modifications such as promoting creativity and a quick sleep. Herein, we discover that infrared radiation from multilayer graphene electrothermal film can obviously promote the appearance of alpha and theta brainwave in human mind. In particular, the occurrence frequency of the alpha and theta waves in EEG can be effectively enhanced up to 2.3 and 3.0 times, respectively. And the duration time of the alpha and theta waves in EEG can also be effectively extended. The mechanism may be attributed to the efficient infrared radiation caused by graphene mainly focused on the range from 7 to 14 micron, coinciding with the radiation wavelength of natural human body, which can be effectively absorbed by the human skin and speed up the blood microcirculation and metabolism. The comparative effect of different working temperature and heating materials such as water, Cu and even monolayer graphene are systematically investigated, indicating the infrared radiation from the multilayer graphene electrothermal film at 50 degrees has the largest enhancement effect of alpha and theta brainwaves. The multilayer graphene film electrical heater represents a convenient and surprising way for triggering the alpha and theta brainwaves, which has many potential applications in the area of enlarged health cerements

Infrared Radiation of Graphene Electrothermal Film Triggered Alpha and Theta Brainwaves

The alpha and theta frequency brainwave activity in electroencephalogram (EEG) signal is proven to correlate with attention, inhibitory processes, memory, perceptual abilities, and sleep. The decreasing of brainwaves is demonstrated to be the reason of aging and even Alzheimer's disease, so triggering alpha and theta brainwave activity may bring positive behavioral modifications such as promoting health care and a quick sleep. Herein, it is discovered that infrared radiation from multilayer graphene electrothermal film can obviously promote the appearance of alpha and theta brainwaves in the human brain. In particular, the occurrence frequency and duration time of the alpha and theta waves in EEG can be effectively enhanced up to 2.3/2.9 and 3.0/4.1 times, respectively. The comparative effect of different working temperatures and heating materials is systematically investigated, indicating efficient infrared radiation from the multilayer graphene electrothermal film, which coincides with the human‐body thermal‐radiation wavelength range from 7 to 14 μm, may be the main mechanism for this enhancement. The multilayer graphene film electrical heater represents a convenient and surprising way for triggering alpha and theta brainwaves, which has many potential applications in the area of enlarged healthcare requirements

Nutritional practice in critically ill COVID-19 patients: A multicenter ambidirectional cohort study in Wuhan and Jingzhou.

BACKGROUND AND OBJECTIVES: The novel coronavirus disease (COVID-19) epidemic is spreading all over the world. With the number of cases increasing rapidly, the epidemiological data on the nutritional practice is scarce. In this study, we aim to describe the clinical characteristics and nutritional practice in a cohort of critically ill COVID-19 patients. METHODS AND STUDY DESIGN: This is a multicenter, ambidirectional cohort study conducted at 11 hospitals in Hubei Province, China. All eligible critical COVID-19 patients in the study hospital intensive care units at 00:00, March 6th, 2020, were included. Data collection was performed via written case report forms. RESULTS: A total of 44 patients were identified and enrolled, of whom eight died during the 28-day outcome follow- up period. The median interval between hospital admission and the study day was 24 (interquartile range, 13- 26) days and 52.2% (23 of 44) of patients were on invasive mechanical ventilation. The median nutrition risk in critically ill (mNUTRIC) score was 3 (interquartile range, 2-5) on the study day. During the enrolment day, 68.2% (30 of 44) of patients received enteral nutrition (EN), while 6.8% (3 of 44) received parenteral nutrition (PN) alone. Nausea and aspiration were uncommon, with a prevalence of 11.4% (5 of 44) and 6.8% (3 of 44), respectively. As for energy delivery, 69.7% (23 of 33) of patients receiving EN and/or PN were achieving their prescribed targets. CONCLUSIONS: The study showed that EN was frequently applied in critical COVID-19 patients. Energy delivery may be suboptimal in this study requiring more attention