Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management

The harsh environment imposes a unique set of challenges on networking strategies. In such circumstances, the environmental impact on network resources and long-time unattended maintenance has not been well investigated yet. To address these challenges, we propose a flexible and adaptive resource management framework that incorporates the environment awareness functionality. In particular, we propose a new network architecture and introduce the new functionalities against the traditional network components. The novelties of the proposed architecture include a deep-learning-based environment resource prediction module and a self-organized service management module. Specifically, the available network resource under various environmental conditions is predicted by using the prediction module. Then based on the prediction, an environment-oriented resource allocation method is developed to optimize the system utility. To demonstrate the effectiveness and efficiency of the proposed new functionalities, we examine the method via an experiment in a case study. Finally, we introduce several promising directions of resource management in harsh environments that can be extended from this paper.Comment: 8 pages, 4 figures, to appear in IEEE Network Magazine, 202

Toxoplasma gondii gra5 deletion mutant protects hosts against Toxoplasma gondii infection and breast tumors

Toxoplasma gondii is the causative agent of toxoplasmosis, a zoonotic disease that poses a threat to human health and a considerable loss to livestock farming. At present, clinical therapeutic drugs mainly target T. gondii tachyzoites and fail to eradicate bradyzoites. Developing a safe and effective vaccine against toxoplasmosis is urgent and important. Breast cancer has become a major public health problem and the therapeutic method needs to be further explored. Many similarities exist between the immune responses caused by T. gondii infection and the immunotherapy for cancers. T. gondii dense granule organelles secrete immunogenic dense granule proteins (GRAs). GRA5 is localized to the parasitophorous vacuole membrane in the tachyzoite stage and the cyst wall in the bradyzoite stage. We found that T. gondii ME49 gra5 knockout strain (ME49Δgra5) was avirulent and failed to form cysts but stimulated antibodies, inflammatory cytokines, and leukocytes infiltration in mice. We next investigated the protective efficacy of ME49Δgra5 vaccination against T. gondii infection and tumor development. All the immunized mice survived the challenge infection of either wild-type RH, ME49, VEG tachyzoites, or ME49 cysts. Moreover, ME49Δgra5 tachyzoite inoculation in situ attenuated the growth of murine breast tumor (4T1) in mice and prevented 4T1’s lung metastasis. ME49Δgra5 inoculation upregulated the levels of Th1 cytokines and tumor-infiltrating T cells in the tumor microenvironment and triggered anti-tumor responses by increasing the number of natural killer, B, and T cells, macrophages, and dendritic cells in the spleen. Collectively, these results suggested that ME49Δgra5 was a potent live attenuated vaccine against T. gondii infection and breast cancer

Observation of many-body Fock space dynamics in two dimensions

Quantum many-body simulation provides a straightforward way to understand fundamental physics and connect with quantum information applications. However, suffering from exponentially growing Hilbert space size, characterization in terms of few-body probes in real space is often insufficient to tackle challenging problems such as quantum critical behavior and many-body localization (MBL) in higher dimensions. Here, we experimentally employ a new paradigm on a superconducting quantum processor, exploring such elusive questions from a Fock space view: mapping the many-body system onto an unconventional Anderson model on a complex Fock space network of many-body states. By observing the wave packet propagating in Fock space and the emergence of a statistical ergodic ensemble, we reveal a fresh picture for characterizing representative many-body dynamics: thermalization, localization, and scarring. In addition, we observe a quantum critical regime of anomalously enhanced wave packet width and deduce a critical point from the maximum wave packet fluctuations, which lend support for the two-dimensional MBL transition in finite-sized systems. Our work unveils a new perspective of exploring many-body physics in Fock space, demonstrating its practical applications on contentious MBL aspects such as criticality and dimensionality. Moreover, the entire protocol is universal and scalable, paving the way to finally solve a broader range of controversial many-body problems on future larger quantum devices.Comment: 8 pages, 4 figures + supplementary informatio

CCL21/CCR7 Prevents Apoptosis via the ERK Pathway in Human Non-Small Cell Lung Cancer Cells

Previously, we confirmed that C-C chemokine receptor 7 (CCR7) promotes cell proliferation via the extracellular signal-regulated kinase (ERK) pathway, but its role in apoptosis of non-small cell lung cancer (NSCLC) cell lines remains unknown. A549 and H460 cells of NSCLC were used to examine the effect of CCL21/CCR7 on apoptosis using flow cytometry. The results showed that activation of CCR7 by its specific ligand, exogenous chemokine ligand 21 (CCL21), was associated with a significant decline in the percent of apoptosis. Western blot and real-time PCR assays indicated that activation of CCR7 significantly caused upregulation of anti-apoptotic bcl-2 and downregulation of pro-apoptotic bax and caspase-3, but not p53, at both protein and mRNA levels. CCR7 small interfering RNA significantly attenuated these effects of exogenous CCL21. Besides, PD98059, a selective inhibitor of MEK that disrupts the activation of downstream ERK, significantly abolished these effects of CCL21/CCR7. Coimmunoprecipitation further confirmed that there was an interaction between p-ERK and bcl-2, bax, or caspase-3, particularly in the presence of CCL21. These results strongly suggest that CCL21/CCR7 prevents apoptosis by upregulating the expression of bcl-2 and by downregulating the expression of bax and caspase-3 potentially via the ERK pathway in A549 and H460 cells of NSCLC

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Path capacity measurement and congestion control in heterogeneous network

The Internet is an open and heterogeneous architecture that consists of different transmission medium, diverse platforms and applications. Network capacity has increased tremendously over the past decade. However, network applications and users have also grown tremendously. The end-to-end throughput of an application is dependent on the bottleneck link capacity along the path as well as the application transport protocol.DOCTOR OF PHILOSOPHY (SCE

MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning

Regulators in gene regulatory networks (GRNs) are crucial for identifying cell states. However, GRN inference based on scRNA-seq data has several problems, including high dimensionality and sparsity, and requires more label data. Therefore, we propose a meta-learning GRN inference framework to identify regulatory factors. Specifically, meta-learning solves the parameter optimization problem caused by high-dimensional sparse data features. In addition, a few-shot solution was used to solve the problem of lack of label data. A structural equation model (SEM) was embedded in the model to identify important regulators. We integrated the parameter optimization strategy into the bi-level optimization to extract the feature consistent with GRN reasoning. This unique design makes our model robust to small-scale data. By studying the GRN inference task, we confirmed that the selected regulators were closely related to gene expression specificity. We further analyzed the GRN inferred to find the important regulators in cell type identification. Extensive experimental results showed that our model effectively captured the regulator in single-cell GRN inference. Finally, the visualization results verified the importance of the selected regulators for cell type recognition