Evaluation of a new high-throughput method for identifying quorum quenching bacteria

Quorum sensing (QS) is a population-dependent mechanism for bacteria to synchronize social behaviors such as secretion of virulence factors. The enzymatic interruption of QS, termed quorum quenching (QQ), has been suggested as a promising alternative anti-virulence approach. In order to efficiently identify QQ bacteria, we developed a simple, sensitive and high-throughput method based on the biosensor Agrobacterium tumefaciens A136. This method effectively eliminates false positives caused by inhibition of growth of biosensor A136 and alkaline hydrolysis of N-acylhomoserine lactones (AHLs), through normalization of beta-galactosidase activities and addition of PIPES buffer, respectively. Our novel approach was successfully applied in identifying QQ bacteria among 366 strains and 25 QQ strains belonging to 14 species were obtained. Further experiments revealed that the QQ strains differed widely in terms of the type ofQQenzyme, substrate specificity and heat resistance. The QQ bacteria identified could possibly be used to control disease in aquaculture

Slow light with a swept-frequency source

We introduce a new concept for stimulated-Brillouin-scattering-based slow light in optical fibers that is applicable for broadly-tunable frequency-swept sources. It allows slow light to be achieved, in principle, over the entire transparency window of the optical fiber. We demonstrate a slow light delay of 10 ns at 1550 nm using a 10-m-long photonic crystal fiber with a source sweep rate of 0.4 MHz/ns and a pump power of 200 mW. We also show that there exists a maximal delay obtainable by this method, which is set by the SBS threshold, independent of sweep rate. For our fiber with optimum length, this maximum delay is ~38 ns, obtained for a pump power of 760 mW.Comment: 6 pages, 5 figure

Dimethylsulfoniopropionate biosynthetic bacteria in the subseafloor sediments of the South China Sea

Dimethylsulfoniopropionate (DMSP) is one of Earth’s most abundant organosulfur molecules, and bacteria in marine sediments have been considered significant producers. However, the vertical profiles of DMSP content and DMSP-producing bacteria in subseafloor sediment have not been described. Here, we used culture-dependent and -independent methods to investigate microbial DMSP production and cycling potential in South China Sea (SCS) sediment. The DMSP content of SCS sediment decreased from 11.25 to 20.90 nmol g–1 in the surface to 0.56–2.08 nmol g–1 in the bottom layers of 8-m-deep subseafloor sediment cores (n = 10). Very few eukaryotic plastid sequences were detected in the sediment, supporting bacteria and not algae as important sediment DMSP producers. Known bacterial DMSP biosynthesis genes (dsyB and mmtN) were only predicted to be in 0.0007–0.0195% of sediment bacteria, but novel DMSP-producing isolates with potentially unknown DMSP synthesis genes and/or pathways were identified in these sediments, including Marinobacter (Gammaproteobacteria) and Erythrobacter (Alphaproteobacteria) sp. The abundance of bacteria with the potential to produce DMSP decreased with sediment depth and was extremely low at 690 cm. Furthermore, distinct DMSP-producing bacterial groups existed in surface and subseafloor sediment samples, and their abundance increased when samples were incubated under conditions known to enrich for DMSP-producing bacteria. Bacterial DMSP catabolic genes were also most abundant in the surface oxic sediments with high DMSP concentrations. This study extends the current knowledge of bacterial DMSP biosynthesis in marine sediments and implies that DMSP biosynthesis is not only confined to the surface oxic sediment zones. It highlights the importance of future work to uncover the DMSP biosynthesis genes/pathways in novel DMSP-producing bacteria

Symmetric electrodes for electrochemical energy-storage devices

Increasing environmental problems and energy challenges have so far attracted urgent demand for developing green and efficient energy-storage systems. Among various energy-storage technologies, sodium-ion batteries (SIBs), electrochemical capacitors (ECs) and especially the already commercialized lithium-ion batteries (LIBs) are playing very important roles in the portable electronic devices or the next-generation electric vehicles. Therefore, the research for finding new electrode materials with reduced cost, improved safety, and high-energy density in these energy storage systems has been an important way to satisfy the ever-growing demands. Symmetric electrodes have recently become a research focus because they employ the same active materials as both the cathode and anode in the same energy-storage system, leading to the reduced manufacturing cost and simplified fabrication process. Most importantly, this feature also endows the symmetric energy-storage system with improved safety, longer lifetime, and ability of charging in both directions. In this Progress Report, we provide the comprehensive summary and comment on different symmetric electrodes and focus on the research about the applications of symmetric electrodes in different energy-storage systems, such as the above mentioned SIBs, ECs and LIBs. Further considerations on the possibility of mass production have also been presented

Stable Sparse Model with Non-Tight Frame

Overcomplete representation is attracting interest in image restoration due to its potential to generate sparse representations of signals. However, the problem of seeking sparse representation must be unstable in the presence of noise. Restricted Isometry Property (RIP), playing a crucial role in providing stable sparse representation, has been ignored in the existing sparse models as it is hard to integrate into the conventional sparse models as a regularizer. In this paper, we propose a stable sparse model with non-tight frame (SSM-NTF) via applying the corresponding frame condition to approximate RIP. Our SSM-NTF model takes into account the advantage of the traditional sparse model, and meanwhile contains RIP and closed-form expression of sparse coefficients which ensure stable recovery. Moreover, benefitting from the pair-wise of the non-tight frame (the original frame and its dual frame), our SSM-NTF model combines a synthesis sparse system and an analysis sparse system. By enforcing the frame bounds and applying a second-order truncated series to approximate the inverse frame operator, we formulate a dictionary pair (frame pair) learning model along with a two-phase iterative algorithm. Extensive experimental results on image restoration tasks such as denoising, super resolution and inpainting show that our proposed SSM-NTF achieves superior recovery performance in terms of both subjective and objective quality

Anonymous Methods Based on Multi-Attribute Clustering and Generalization Constraints

The dissemination and sharing of data sheets in IoT applications presents privacy and security challenges that can be addressed using the k-anonymization algorithm. However, this method needs improvement, for example, in areas related to its overgeneralization and its insufficient attribute diversity constraints during the anonymization process. To address these issues, this study proposes a multi-attribute clustering and generalization constraints (k,l)-anonymization method that can be applied to multidimensional data tables. The algorithm first used a greedy strategy to rank the attributes by width first, derived the division into dimensions to construct a multidimensional generalization hierarchy, and then selected the attributes with the most significant width values as the priority generalization attributes. Next, the k-nearest neighbor (KNN) clustering method was introduced to determine the initial clustering center by the width-first results, divide the quasi-identifier attributes into KNN clusters according to a distance metric, and generalize the quasi-identifier attributes in the equivalence class using a hierarchical generalization structure. Then, the proposed method re-evaluated the attributes to be generalized before each generalization operation. Finally, the algorithm employed an improved frequency–diversity constraint to generalize sensitive attributes in order to ensure that there were at least l records that were mutually dissimilar and closest in the equivalence class. While limiting the frequency threshold for the occurrence of sensitive attributes, the sensitive attribute values remained similar within the group, thus achieving protection of anonymity for all the attributes

Effects of Gestational Sleep Patterns and Their Changes on Maternal Glycemia and Offspring Physical Growth in Early Life

Maternal sleep patterns during pregnancy are drawing increased attention to examine its role in the regulation of maternal glycemia and physical growth of offspring within 24 months. Among 3329 eligible mother–child pairs included in the Shanghai Maternal–Child Pairs Cohort, sleep patterns of pregnant women were assessed by Pittsburgh Sleep Quality Index and objective measurement in early and late pregnancy. Offspring physical growth within 24 months was primarily indicated by the body mass index Z-score (BAZ), catch-up growth, and overweight/obesity. In total, 3329 and 382 pregnant women were included with subjectively assessed and objectively measured sleep pattern, respectively. The increased risk of GDM was associated with maternal night-time sleep duration ≥8.5 h in early pregnancy, or sleep quality change from poor to good during pregnancy (OR = 1.48; 95% CI, 1.06 to 2.07). In the GDM group, the effect of sleep duration in early pregnancy on overweight/obesity in offspring within 24 months showed a U-shaped curve, with a 1.73-fold and 1.43-fold increased risk of overweight/obesity of offspring in pregnant women with <7.5 or ≥8.5 h of sleep duration, respectively. A good gestational sleep pattern was required to reduce the risk of GDM and offspring overweight/obesity within 24 months

Si-containing precursors for Si-based anode materials of Li-ion batteries: A review

Lithium-ion batteries with high energy density are in demand for consumer electronics, electric vehicles, and grid-scale stationary energy storage. Si is one of the most promising anode materials due to its extremely high specific capacity. However, the full application of Si-based anode materials is limited by poor cycle life and rate capability resulted from low ionic/electronic conductivity and large volume change over cycling. In recent years, great progress has been made in improving the performance of Si anodes by employing nanotechnology. The preparation methods are essentially important, in which the precursors used are crucial to design and control the microstructure for the Si-based materials. In this review, we provide comprehensive summary and comment on different Si-containing precursors for preparation of nanosized Si-based anode materials and focus on the corresponding electrochemical performances in lithium-ion batteries. Bulk sized silicon, silicon wafer and silicon microparticles are generally used as starting materials to synthesize porous or nanosized silicon, and the routes for the synthesis are rather mature and commercially available. Silica is also commonly used to form silicon by conversion through a facile magnesiothermic reduction. Silica derivation from natural resources, especially from rice husks, is much more sustainable and lower cost than alternative methods, which attracts considerable research attention. In addition, gaseous Si-based sources like SiH4, Si2H6 and SiHxCly, liquid silicon sources like trisilane and phenylsilane and elemental silicon have successfully used to prepare nanosized or carbon-coated silicon. Further considerations on massive production possibility have also been presented