Options to Improve the Mechanical Properties of Protein-Based Materials

While bio-based but chemically synthesized polymers such as polylactic acid require industrial conditions for biodegradation, protein-based materials are home compostable and show high potential for disposable products that are not collected. However, so far, such materials lack in their mechanical properties to reach the requirements for, e.g., packaging applications. Relevant measures for such a modification of protein-based materials are plasticization and cross-linking; the former increasing the elasticity and the latter the tensile strength of the polymer matrix. The assessment shows that compared to other polymers, the major bottleneck of proteins is their complex structure, which can, if developed accordingly, be used to design materials with desired functional properties. Chemicals can act as cross-linkers but require controlled reaction conditions. Physical methods such as heat curing and radiation show higher effectiveness but are not easy to control and can even damage the polymer backbone. Concerning plasticization, effectiveness and compatibility follow opposite trends due to weak interactions between the plasticizer and the protein. Internal plasticization by covalent bonding surpasses these limitations but requires further research specific for each protein. In addition, synergistic approaches, where different plasticization/cross-linking methods are combined, have shown high potential and emphasize the complexity in the design of the polymer matrix.

Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials

Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that provides 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating realistic coordinates for polymer melt simulations, single-stranded as well as circular single-stranded DNA. We further demonstrate the power of our approach by setting up a microphase-separated block copolymer system, and by generating a liquid-liquid phase separated system inside a lipid vesicle.

A Review of Microbial Mediated Iron Nanoparticles (IONPs) and Its Biomedical Applications

Nanotechnology is a booming avenue in science and has a multitude of applications in health, agriculture, and industry. It exploits materials’ size at nanoscale (1–100 nm) known as nanoparticles (NPs). These nanoscale constituents are made via chemical, physical, and biological methods; however, the biological approach offers multiple benefits over the other counterparts. This method utilizes various biological resources for synthesis (microbes, plants, and others), which act as a reducing and capping agent. Among these sources, microbes provide an excellent platform for synthesis and have been recently exploited in the synthesis of various metallic NPs, in particular iron. Owing to their biocompatible nature, superparamagnetic properties, small size efficient, permeability, and absorption, they have become an integral part of biomedical research. This review focuses on microbial synthesis of iron oxide nanoparticles using various species of bacteria, fungi, and yeast. Possible applications and challenges that need to be addressed have also been discussed in the review; in particular, their antimicrobial and anticancer potentials are discussed in detail along with possible mechanisms. Moreover, some other possible biomedical applications are also highlighted. Although iron oxide nanoparticles have revolutionized biomedical research, issues such as cytotoxicity and biodegradability are still a major bottleneck in the commercialization of these nanoparticle-based products. Addressing these issues should be the topmost priority so that the biomedical industry can reap maximum benefit from iron oxide nanoparticle-based products.

Research on Energy Efficiency Management of Forklift Based on Improved YOLOv5 Algorithm

As an important tool for loading, unloading, and distributing palletized goods, forklifts are widely used in different links of industrial production process. However, due to the rapid increase in the types and quantities of goods, item statistics have become a major bottleneck in production. Based on machine vision, the paper proposes a method to count the amount of goods loaded and unloaded within the working time limit to analyze the efficiency of the forklift. The proposed method includes the data preprocessing section and the object detection section. In the data preprocessing section, through operations such as framing and clustering the collected video data and using the improved image hash algorithm to remove similar images, a new dataset of forklift goods was built. In the object detection section, the attention mechanism and the replacement network layer were used to improve the performance of YOLOv5. The experimented results showed that, compared with the original YOLOv5 model, the improved model is lighter in size and faster in detection speed without loss of detection precision, which could also meet the requirements for real-time statistics on the operation efficiency of forklifts.

POET (v0.1): speedup of many-core parallel reactive transport simulations with fast DHT lookups

Coupled reactive transport simulations are extremely demanding in terms of required computational power, which hampers their application and leads to coarsened and oversimplified domains. The chemical sub-process represents the major bottleneck: its acceleration is an urgent challenge which gathers increasing interdisciplinary interest along with pressing requirements for subsurface utilization such as spent nuclear fuel storage, geothermal energy and CO2 storage. In this context we developed POET (POtsdam rEactive Transport), a research parallel reactive transport simulator integrating algorithmic improvements which decisively speed up coupled simulations. In particular, POET is designed with a master/worker architecture, which ensures computational efficiency in both multicore and cluster compute environments. POET does not rely on contiguous grid partitions for the parallelization of chemistry but forms work packages composed of grid cells distant from each other. Such scattering prevents particularly expensive geochemical simulations, usually concentrated in the vicinity of a reactive front, from generating load imbalance between the available CPUs (central processing units), as is often the case with classical partitions. Furthermore, POET leverages an original implementation of the distributed hash table (DHT) mechanism to cache the results of geochemical simulations for further reuse in subsequent time steps during the coupled simulation. The caching is hence particularly advantageous for initially chemically homogeneous simulations and for smooth reaction fronts. We tune the rounding employed in the DHT on a 2D benchmark to validate the caching approach, and we evaluate the performance gain of POET's master/worker architecture and the DHT speedup on a 3D benchmark comprising around 650 000 grid elements. The runtime for 200 coupling iterations, corresponding to 960 simulation days, reduced from about 24 h on 11 workers to 29 min on 719 workers. Activating the DHT reduces the runtime further to 2 h and 8 min respectively. Only with these kinds of reduced hardware requirements and computational costs is it possible to realistically perform the long-term complex reactive transport simulations, as well as perform the uncertainty analyses required by pressing societal challenges connected with subsurface utilization.

Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome

Mass spectrometry (MS) based immunopeptidomics is used in several biomedical applications including neo-epitope discovery in oncology and next-generation vaccine development. Immunopeptidome data are highly complex given the expression of multiple HLA alleles on the cell membrane and presence of co-immunoprecipitated contaminants. The absence of tools that accurately deal with these challenges is currently a major bottleneck for the large-scale application of this technique. Here, we present the MHCMotifDecon that benefits from state-of-the-art HLA class-I and class-II predictions to accurately deconvolute immunopeptidome datasets and assign individual ligands to the most likely HLA allele while discarding co-purified contaminants. We have benchmarked the tool against other state-of-the-art methods and illustrated its application on experimental datasets for HLA-DR demonstrating a previously underappreciated role for HLA-DRB3/4/5 molecules in defining HLA class II immune repertoires. With its ease of use MHCMotifDecon can efficiently guide interpretation of immunopeptidome datasets, serving the discovery of novel T cell targets.

LiveCellMiner: A New Tool to Analyze Mitotic Progression

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

Insights Into Mechanism of the Naphthalene-Enhanced Biodegradation of Phenanthrene by Pseudomonas sp. SL-6 Based on Omics Analysis

The existence of polycyclic aromatic hydrocarbons (PAHs) in contaminated environment is multifarious. At present, studies of metabolic regulation focus on the degradation process of single PAH. The global metabolic regulatory mechanisms of microorganisms facing coexisting PAHs are poorly understood, which is the major bottleneck for efficient bioremediation of PAHs pollution. Naphthalene (NAP) significantly enhanced the biodegradation of phenanthrene (PHE) by Pseudomonas sp. SL-6. To explore the underlying mechanism, isobaric tags for relative and absolute quantification (iTRAQ) labeled quantitative proteomics was used to characterize the differentially expressed proteins of SL-6 cultured with PHE or NAP + PHE as carbon source. Through joint analysis of proteome and genome, unique proteins were identified and quantified. The up-regulated proteins mainly concentrated in PAH catabolism, Transporters and Electron transfer carriers. In the process, the regulator NahR, activated by salicylate (intermediate of NAP-biodegradation), up-regulates degradation enzymes (NahABCDE and SalABCDEFGH), which enhances the biodegradation of PHE and accumulation of toxic intermediate–1-hydroxy-2-naphthoic acid (1H2Na); 1H2Na stimulates the expression of ABC transporter, which maintains intracellular physiological activity by excreting 1H2Na; the up-regulation of cytochrome C promotes the above process running smoothly. Salicylate works as a trigger that stimulates cell to respond globally. The conjecture was verified at transcriptional and metabolic levels. These new insights contribute to improving the overall understanding of PAHs-biodegradation processes under complex natural conditions, and promoting the application of microbial remediation technology for PAHs pollution.

Fast alignment of reads to a variation graph with application to SNP detection

Sequencing technologies has provided the basis of most modern genome sequencing studies due to its high base-level accuracy and relatively low cost. One of the most demanding step is mapping reads to the human reference genome. The reliance on a single reference human genome could introduce substantial biases in downstream analyses. Pangenomic graph reference representations offer an attractive approach for storing genetic variations. Moreover, it is possible to include known variants in the reference in order to make read mapping, variant calling, and genotyping variant-aware. Only recently a framework for variation graphs, vg [Garrison E, Adam MN, Siren J, et al. Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875–9], have improved variation-aware alignment and variant calling in general. The major bottleneck of vg is its high cost of reads mapping to a variation graph. In this paper we study the problem of SNP calling on a variation graph and we present a fast reads alignment tool, named VG SNP-Aware. VG SNP-Aware is able align reads exactly to a variation graph and detect SNPs based on these aligned reads. The results show that VG SNP-Aware can efficiently map reads to a variation graph with a speedup of 40× with respect to vg and similar accuracy on SNPs detection.

Multi-attribute Glycan Identification and FDR Control for Glycoproteomics

Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward, however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collision or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B- (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with FDR control of both peptide and glycan components that is both sensitive and robust against false identifications.