Roadmap for Sustainable Mixed Ionic‐Electronic Conducting Membranes

Mixed ionic‐electronic conducting (MIEC) membranes have gained growing interest recently for various promising environmental and energy applications, such as H₂ and O₂ production, CO₂ reduction, O₂ and H₂ separation, CO₂ separation, membrane reactors for production of chemicals, cathode development for solid oxide fuel cells, solar‐driven evaporation and energy‐saving regeneration as well as electrolyzer cells for power‐to‐X technologies. The purpose of this roadmap, written by international specialists in their fields, is to present a snapshot of the state‐of‐the‐art, and provide opinions on the future challenges and opportunities in this complex multidisciplinary research field. As the fundamentals of using MIEC membranes for various applications become increasingly challenging tasks, particularly in view of the growing interdisciplinary nature of this field, a better understanding of the underlying physical and chemical processes is also crucial to enable the career advancement of the next generation of researchers. As an integrated and combined article, it is hoped that this roadmap, covering all these aspects, will be informative to support further progress in academics as well as in the industry‐oriented research toward commercialization of MIEC membranes for different applications

Traffic Classification Method by Combination of Host Behaviour and Statistical Approach

Traffic classification, one of the most active fields in Internet traffic research, is the substructure of network design and management. Generally, there are four techniques to identify the traffic, port-based, payload-based, flow statistic-based, and host-based approaches. In this paper, a hybrid method to classify the traffic was proposed combining the host behaviour and the Affinity Propagation (AP) algorithm. Simple features in the statistical process were selected at the first stage of classification; then, the initial classification results and the host behaviour model were combined to generate the final results. The host behaviour model was updated by the feedback of previous classification. The combining classification approach was evaluated on two real traces. The results indicated that the proposed technique offered improved performance compared with BLINC and independent AP algorithms

Evaluation of Mycobacterium tuberculosis-specific antibody responses for the discrimination of active and latent tuberculosis infection

Objectives: The serological antibody detection tests offer several advantages for the rapid diagnosis of tuberculosis (TB). The Mycobacterium tuberculosis-specific antibody responses associated with different stages of TB infection remain to be investigated. Methods: The Pathozyme-Myco IgG (Myco G), Pathozyme TB Complex Plus (TB Complex), IBL M. tuberculosis IgG ELISA (IBL), Anda Biologicals TB IgG (Anda-TB), and T-SPOT.TB (T-SPOT) tests were performed for 133 active TB patients (ATB group), 131 controls (CON group), and 95 subjects with latent TB infection (LTBI group). Results: The four serological tests all showed relatively low sensitivity in the ATB group but high specificity in the LTBI and CON groups. The antibody levels of the four serological tests were significantly higher in the ATB group than in the LTBI group. The same trend was observed between the LTBI and CON groups. The four serological tests demonstrated potential diagnostic value in discriminating ATB from LTBI. A combination of the Anda-TB and TB Complex tests exhibited the best diagnostic potential in discriminating ATB from LTBI, with a sensitivity of 89.4% and a specificity of 94.7%. Further, the diagnostic value of Anda-TB and TB Complex were validated in a prospective cohort including 106 patients with suspected ATB. Combined with the T-SPOT test, the tests showed a sensitivity of 87.2% and a specificity of 92.5% for discriminating ATB patients from all ATB suspected cases in the validation group. Conclusions: The antibody responses of the serological tests all showed significant differences between the ATB and LTBI groups. A combination of Anda-TB and the TB Complex test demonstrated high diagnostic potential in discriminating ATB from LTBI and may be an additional diagnostic tool in the diagnosis of M. tuberculosis infection. Keywords: Tuberculosis, Latent infection, Serodiagnosi

IL-2/IFN-γ ratio in subjects with active TB (ATB group) and in subjects with latent TB infection (LTBI group).

<p>A. IL-2/IFN-γ ratio in response to TB antigens was significantly higher in LTBI group than in the ATB group (p<0.0001). Horizontal lines indicate the median IL-2/IFN-γ ratio. B. The ROC analysis using subjects with active TB as patients and subjects with latent TB as controls.</p

The un-stimulated and TB antigen-stimulated expression of IFN-γ, IP-10, IL-2 and TNF-α in patients with active tuberculosis (ATB group), household contacts (HHC group) and healthy controls (HC group).

<p>The expression of IFN-γ (A and E), IP-10 (B and F), IL-2 (C and G) and TNF-α (D and H) were determined using ELISA and expressed in pg/ml. The horizontal line indicates the median amount of biomarker production.</p

Demographic and clinical characteristics of the study population.

<p>ATB, subjects with active tuberculosis; HHC, household contact; HC health control.</p

Distribution of positive, negative and indeterminate responders with the QFT, IP-10 and IL-2 test and when the tests are combined: (A) active tuberculosis (ATB group), (B) household contacts (HHC group) and (C) healthy controls (HC group).

<p>In the combination test, a positive responder was determined if any of the tests was positive. All the detection rate were compared with QFT by McNemars test, a: p<0.001, b: p<0.05.</p

Logistic regression analysis of the association of positive results of QFT, IP-10, IL-2 and TST with potential risk factors relevant to <i>Mtb</i> infection in HHC group.

<p>OR: odds ratio; 95%CI: 95% confidence interval.</p>a<p>Median age: 41 years old, the median age calculated in HHC group.</p

Levels of IFN-γ, IP-10, IL-2 and TNF-α released in un-stimulated, TB antigen-stimulated and mitogen-stimulated plasma.

<p>Data are presented as median concentration in pg/ml (interquartile range).</p><p>Statistical significance was determined versus ATB group,</p>a<p>p<0.001;^b0.001≤p<0.01;^c0.01≤p<0.05.(Kruskal-Wallist test).</p><p>Statistical significance was determined between HC and HHC group or between NTB and LTBI group, ^d: p<0.001; ^e: 0.001≤p<0.01; ^f: 0.01≤p<0.05. (Kruskal-Wallist test).</p