An investigation into the bacterial community structure of an electricity-generating microbial fuel cell

It was observed that Proteobacteria sequences were present at higher numbers within clone libraries from the bacterial clump and electrode biofilm compared to other parts of the fuel cell. It was also observed that results of the 16S rRNA gene libraries were in excellent agreement with those of the in situ probing and denaturing gradient gel electrophoresis (DGGE) analysis. DGGE analysis also showed that the enriched bacterial populations were different from those fond in the original activated sludge used to inoculate the microbial fuel cell at the start of the experiment. Conventional microbiological methods revealed that members of the family Enterobacteriaceae, such as Klebsiella sp. and Enterobacter sp. and of the Proteobacteria with Fe(III)-reduction and electrochemical activity had a significant potential for energy generation in this system

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition–transition–distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews’ correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2–5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.

First Demonstration of Ultra-Thin SiGe-Channel Junctionless Accumulation-Mode (JAM) Bulk FinFETs on Si Substrate with PN Junction-Isolation Scheme

A SiGe-channel junctionless-accumulation-mode (JAM) PMOS bulk FinFETs were successfully demonstrated on Si substrate with PN junction-isolation scheme for the first time. The JAM bulk FinFETs with fin width of 18 nm exhibits excellent subthreshold characteristics such as subthreshold swing of 64 mV/decade, drain-induced barrier lowering (DIBL) of 40 mV/V and high Ion/Ioff current ratio ( \u3e 1 x 105). The change of substrate bias from 0 to 5 V leads to the threshold voltage shift of 53 mV by modulating the effective channel thickness. When compared to the Si-channel bulk FinFETs with fin width of 18 nm, Si and SiGe channel devices exhibits comparable subthreshold swing and DIBL. For devices with longer fin width, SiGe channel devices exhibits much lower DIBL, indicating superior top-gate controllability and robustness to substrate bias compared to the Si channel devices. A zero temperature coefficient point was observed in the transfer curves as temperature increases from -120 to 120°C, confirming that mobility degradation is dominantly affected by phonon scattering mechanism

Anticancer Efficacy of Cordyceps militaris

Cordyceps militaris is used widely as a traditional medicine in East Asia. Although a few studies have attempted to elucidate the anticancer activities of C. militaris, the precise mechanism of C. militaris therapeutic effects is not fully understood. We examined the anticancer activities of C. militaris ethanolic extract (Cm-EE) and its cellular and molecular mechanisms. For this purpose, a xenograft mouse model bearing murine T cell lymphoma (RMA) cell-derived cancers was established to investigate in vivo anticancer mechanisms. MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay, immunoblotting analysis, and flow cytometric assay were employed to check in vitro cytotoxicity, molecular targets, and proapoptotic action of Cm-EE. Interestingly, cancer sizes and mass were reduced in a C. militaris-administered group. Levels of the phosphorylated forms of p85 and AKT were clearly decreased in the group administered with Cm-EE. This result indicated that levels of phosphoglycogen synthase kinase 3β (p-GSK3β) and cleaved caspase-3 were increased with orally administered Cm-EE. In addition, Cm-EE directly inhibited the viability of cultured RMA cells and C6 glioma cells. The number of proapoptotic cells was significantly increased in a Cm-EE treated group compared with a control group. Our results suggested that C. militaris might be able to inhibit cancer growth through regulation of p85/AKT-dependent or GSK3β-related caspase-3-dependent apoptosis

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.Comment: 21 pages, 4 figure