Membrane gas separation has potential for the recovery and purification of helium, because the majority of membranes have selectivity for helium. This review reports on the current state of the research and patent literature for membranes undertaking helium separation. This includes direct recovery from natural gas, as an ancillary stage in natural gas processing, as well as niche applications where helium recycling has potential. A review of the available polymeric and inorganic membranes for helium separation is provided. Commercial gas separation membranes in comparable gas industries are discussed in terms of their potential in helium separation. Also presented are the various membrane process designs patented for the recovery and purification of helium from various sources, as these demonstrate that it is viable to separate helium through currently available polymeric membranes. This review places a particular focus on those processes where membranes are combined in series with another separation technology, commonly pressure swing adsorption. These combined processes have the most potential for membranes to produce a high purity helium product. The review demonstrates that membrane gas separation is technically feasible for helium recovery and purification, though membranes are currently only applied in niche applications focused on reusing helium rather than separation from natural sources.

In recent years, spider silk-based materials have attracted attention because of their biocompatibility, processability, and biodegradability. For their potential use in biomaterial applications, i.e., as drug delivery systems and implant coatings for tissue regeneration, it is vital to understand the interactions between the silk biomaterial surface and the biological environment. Like most polymeric carrier systems, spider silk material surfaces can adsorb proteins when in contact with blood, resulting in the formation of a biomolecular corona. Here, we assessed the effect of surface net charge of materials made of recombinant spider silk on the biomolecular corona composition. In-depth proteomic analysis of the biomolecular corona revealed that positively charged spider silk materials surfaces interacted predominantly with fibrinogen-based proteins. This fibrinogen enrichment correlated with blood clotting observed for both positively charged spider silk films and particles. In contrast, negative surface charges prevented blood clotting. Genetic engineering allows the fine-tuning of surface properties of the spider silk particles providing a whole set of recombinant spider silk proteins with different charges or peptide tags to be used for, for example, drug delivery or cell docking, and several of these were analyzed concerning the composition of their biomolecular corona. Taken together this study demonstrates how the surface net charge of recombinant spider silk surfaces affects the composition of the biomolecular corona, which in turn affects macroscopic effects such as fibrin formation and blood clotting.

There is a need for improved nanomaterials to simultaneously target cancer cells and avoid non‐specific clearance by phagocytes. An ellipsoidal polymersome system is developed with a unique tunable size and shape property. These particles are functionalized with in‐house phage‐display cell‐targeting peptide to target a medulloblastoma cell line in vitro. Particle association with medulloblastoma cells is modulated by tuning the peptide ligand density on the particles. These polymersomes has low levels of association with primary human blood phagocytes. The stealth properties of the polymersomes are further improved by including the peptide targeting moiety, an effect that is likely driven by the peptide protecting the particles from binding blood plasma proteins. Overall, this ellipsoidal polymersome system provides a promising platform to explore tumor cell targeting in vivo.

Chaplin F (Chp F) is a secreted surface-active peptide involved in the aerial growth of Streptomyces. While Chp E demonstrates a pH-responsive surface activity, the relationship between Chp F structure, function and the effect of solution pH is unknown. Chp F peptides were found to self-assemble into amyloid fibrils at acidic pH (3.0 or the isoelectric point (pI) of 4.2), with ~99% of peptides converted into insoluble fibrils. In contrast, Chp F formed short assemblies containing a mixture of random coil and β-sheet structure at a basic pH of 10.0, where only 40% of the peptides converted to fibrils. The cysteine residues in Chp F did not appear to play a role in fibril assembly. The interfacial properties of Chp F at the air/water interface were altered by the structures adopted at different pH, with Chp F molecules forming a higher surface-active film at pH 10.0 with a lower area per molecule compared to Chp F fibrils at pH 3.0. These data show that the pH responsiveness of Chp F surface activity is the reverse of that observed for Chp E, which could prove useful in potential applications where surface activity is desired over a wide range of solution pH.

The self-association of proteins into amyloid fibrils offers an alternative to the natively folded state of many polypeptides. Although commonly associated with disease, amyloid fibrils represent the natural functional state of some proteins, such as the chaplins from the soil-dwelling bacterium Streptomyces coelicolor, which coat the aerial mycelium and spores rendering them hydrophobic. We have undertaken a biophysical characterisation of the five short chaplin peptides ChpD-H to probe the mechanism by which these peptides self-assemble in solution to form fibrils. Each of the five chaplin peptides produced synthetically or isolated from the cell wall is individually surface-active and capable of forming fibrils under a range of solution conditions in vitro. These fibrils contain a highly similar cross-β core structure and a secondary structure that resembles fibrils formed in vivo on the spore and mycelium surface. They can also restore the growth of aerial hyphae to a chaplin mutant strain. We show that cysteine residues are not required for fibril formation in vitro and propose a role for the cysteine residues conserved in four of the five short chaplin peptides.

Streptomyces bacteria form reproductive aerial hyphae that are covered with a pattern of pairwise aligned fibrils called rodlets. The presence of the rodlet layer requires two homologous rodlin proteins, RdlA and RdlB, and the functional amyloid chaplin proteins, ChpA-H. In contrast to the redundancy shared among the eight chaplins, both RdlA and RdlB are indispensable for the establishment of this rodlet structure. By using a comprehensive biophysical approach combined with in vivo characterization we found that RdlB, but not RdlA, readily assembles into amyloid fibrils. The marked difference in amyloid propensity between these highly similar proteins could be largely attributed to a difference in amino acid sequence at just three sites. Further, an engineered RdlA protein in which these three key amino acids were replaced with the corresponding residues from RdlB could compensate for loss of RdlB and restore formation of the surface-exposed amyloid layer in bacteria. Our data reveal that RdlB is a new functional amyloid and provide a biophysical basis for the functional differences between the two rodlin proteins. This study enhances our understanding of how rodlin proteins contribute to formation of an outer fibrillar layer during spore morphogenesis in streptomycetes.

An enzymatic biosynthesis approach is described for codeine, the most widely used medicinal opiate, providing a more environmentally sustainable alternative to current chemical conversion, with yields and productivity compatible with industrial production. Escherichia coli strains were engineered to express key enzymes from poppy, including the recently discovered neopinone isomerase, producing codeine from thebaine. We show that compartmentalization of these enzymes in different cells is an effective strategy that allows active spatial and temporal control of reactions, increasing yield and volumetric productivity and reducing byproduct generation. Codeine is produced at a yield of 64% and a volumetric productivity of 0.19 g/(L·h), providing the basis for an industrially applicable aqueous whole-cell biotransformation process. This approach could be used to redirect thebaine-rich feedstocks arising from the U.S. reduction of opioid manufacturing quotas or applied to enable total biosynthesis and may have broader applicability to other medicinal plant compounds.

The cytoskeletal mechanics and cell mechanical properties play an important role in cellular behaviors. In this study, in order to provide comprehensive insights into the relationship between different cytoskeletal components and cellular elastic moduli, we built a phase-modulated surface acoustic wave microfluidic device to measure cellular compressibility and a microfluidic micropipette-aspiration device to measure cellular Young's modulus. The microfluidic devices were validated based on experimental data and computational simulations. The contributions of structural cytoskeletal actin filament and microtubule to cellular compressibility and Young's modulus were examined in MCF-7 cells. The compressibility of MCF-7 cells was increased after microtubule disruption, whereas actin disruption had no effect. In contrast, Young's modulus of MCF-7 cells was reduced after actin disruption but unaffected by microtubule disruption. The actin filaments and microtubules were stained to confirm the structural alteration in cytoskeleton. Our findings suggest the dissimilarity in the structural roles of actin filaments and microtubules in terms of cellular compressibility and Young's modulus. Based on the differences in location and structure, actin filaments mainly contribute to tensile Young's modulus and microtubules mainly contribute to compressibility. In addition, different responses to cytoskeletal alterations between acoustophoresis and micropipette aspiration demonstrated that micropipette aspiration was better at detecting the change from actin cortex, while the response to acoustophoresis was governed by microtubule networks.

Assessing biodiversity is an important step in the study of microbial ecology associated with a given environment. Multiple indices have been used to quantify species diversity, which is a key biodiversity measure. Measuring species diversity of viruses in different environments remains a challenge relative to measuring the diversity of other microbial communities. Metagenomics has played an important role in elucidating viral diversity by conducting metavirome studies; however, metavirome data are of high complexity requiring robust data preprocessing and analysis methods. In this review, existing bioinformatics methods for measuring species diversity using metavirome data are categorised broadly as either sequence similarity-dependent methods or sequence similarity-independent methods. The former includes a comparison of DNA fragments or assemblies generated in the experiment against reference databases for quantifying species diversity, whereas estimates from the latter are independent of the knowledge of existing sequence data. Current methods and tools are discussed in detail, including their applications and limitations. Drawbacks of the state-of-the-art method are demonstrated through results from a simulation. In addition, alternative approaches are proposed to overcome the challenges in estimating species diversity measures using metavirome data.

BACKGROUND: In metagenomics, the separation of nucleotide sequences belonging to an individual or closely matched populations is termed binning. Binning helps the evaluation of underlying microbial population structure as well as the recovery of individual genomes from a sample of uncultivable microbial organisms. Both supervised and unsupervised learning methods have been employed in binning; however, characterizing a metagenomic sample containing multiple strains remains a significant challenge. In this study, we designed and implemented a new workflow, Coverage and composition based binning of Metagenomes (CoMet), for binning contigs in a single metagenomic sample. CoMet utilizes coverage values and the compositional features of metagenomic contigs. The binning strategy in CoMet includes the initial grouping of contigs in guanine-cytosine (GC) content-coverage space and refinement of bins in tetranucleotide frequencies space in a purely unsupervised manner. With CoMet, the clustering algorithm DBSCAN is employed for binning contigs. The performances of CoMet were compared against four existing approaches for binning a single metagenomic sample, including MaxBin, Metawatt, MyCC (default) and MyCC (coverage) using multiple datasets including a sample comprised of multiple strains. RESULTS: Binning methods based on both compositional features and coverages of contigs had higher performances than the method which is based only on compositional features of contigs. CoMet yielded higher or comparable precision in comparison to the existing binning methods on benchmark datasets of varying complexities. MyCC (coverage) had the highest ranking score in F1-score. However, the performances of CoMet were higher than MyCC (coverage) on the dataset containing multiple strains. Furthermore, CoMet recovered contigs of more species and was 18 - 39% higher in precision than the compared existing methods in discriminating species from the sample of multiple strains. CoMet resulted in higher precision than MyCC (default) and MyCC (coverage) on a real metagenome. CONCLUSIONS: The approach proposed with CoMet for binning contigs, improves the precision of binning while characterizing more species in a single metagenomic sample and in a sample containing multiple strains. The F1-scores obtained from different binning strategies vary with different datasets; however, CoMet yields the highest F1-score with a sample comprised of multiple strains.

Background: Seizure clusters and “bursts” are of clinical importance. Clusters are reported to be a marker of antiepileptic drug resistance. Additionally, seizure clustering has been found to be associated with increased morbidity and mortality. However, there are no statistical methods described in the literature to delineate bursting phenomenon in epileptic seizures. Methods: We present three automatic burst detection methods referred to as precision constrained grouping (PCG), burst duration constrained grouping (BCG), and interseizure interval constrained grouping (ICG). Concordance correlation coefficients were used to confirm the pairwise agreement between common bursts isolated using these three automatic burst detection procedures. Additionally, three graphical methods were employed to demonstrate seizure bursts: modified scatter plots, staircase plots, and dropline plots. Burst detection procedures are demonstrated on data from continuous intracranial ambulatory EEG monitoring in a patient diagnosed with drug-refractory focal epilepsy. Results: We analyzed 1,569 seizures, from our assigned index patient, captured on ambulatory intracranial EEG monitoring. A total of 31, 32, and 32 seizure bursts were detected by the three quantitative methods (BCG, ICG, and PCG), respectively. The concordance correlation coefficient was ≥0.99 signifying considerably stronger than chance burst detector agreements with one another. Conclusions: Bursting is a quantifiable temporal phenomenon in epilepsy and seizure bursts can be reliably detected using our methodology.

Background: There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting. Methods: Using three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker (n = 3016), Human Epilepsy Project (n = 93), and NeuroVista (n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions. Results: A consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation (R2 > 0.83). The three datasets showed high predictive accuracy for this log-log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log-log predicted 94% of the correct ranges while the RR50 predicted 77%. Conclusion: Reliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice.