Search for: All records

Cryo-electron Tomography (cryo-ET) generates 3D visualization of cellular organization that allows biologists to analyze cellular structures in a near-native state with nano resolution. Recently, deep learning methods have demonstrated promising performance in classification and segmentation of macromolecule structures captured by cryo-ET, but training individual deep learning models requires large amounts of manually labeled and segmented data from previously observed classes. To perform classification and segmentation in the wild (i.e., with limited training data and with unseen classes), novel deep learning model needs to be developed to classify and segment unseen macromolecules captured by cryo-ET. In this paper, we develop a one-shot learning framework, called cryo-ET one-shot network (COS-Net), for simultaneous classification of macromolecular structure and generation of the voxel-level 3D segmentation, using only one training sample per class. Our experimental results on 22 macromolecule classes demonstrated that our COS-Net could efficiently classify macromolecular structures with small amounts of samples and produce accurate 3D segmentation at the same time. 

Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ . However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components. 

Phosphonium‐containing polyelectrolyte networks (PENs) (P1–P4) were prepared by cyclotrimerization of bis(4‐acetylphenyl)diphenylphosphonium bromide (M1) and 1,4‐diacetylbenzene (M2) withp‐toluene sulfonic acid in variousM1:M2ratios (1,0, 1:1, 1:2, and 1:4). The relative abundance of the PAr₄⁺units in each PEN was demonstrated to influence thermal stability, alkaline stability, water uptake, surface area, and CO₂uptake in predictable ways. Impressively, PENs with NTf₂⁻counterions (Tf = CF₃SO₃) did not exhibit 5% mass loss until heating above 400 °C. Alkaline stability, tested by challenging a PEN with 6MNaOH(aq) at 65 °C for 120 h, increased with increasing PAr₄⁺content, which reflected the enhanced reactivity of the HO⁻anion in more hydrophobic materials (i.e., PENs with lowerM1:M2ratios). The specific surface areas estimated by Brunauer‐Emmett‐Teller (BET) analysis for these PENs were above 60 m²/g under N₂and nearly 90 m²/g under CO₂. Notably,P3(in which 33% of monomers comprise a phosphonium moiety) exhibited a CO₂uptake affinity of one CO₂molecule adsorbed for every phosphonium site. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem.2019,57, 598–604

 

A series of 10 polythiophene derivatives is reported, in which each polymer has a different percentage of carboxylic acid‐bearing repeat units. The properties of these polymers are explored under acidic conditions, where the carboxylic acid moieties remain neutral, and under basic conditions, where the carboxylic acid units become anionic carboxylates. The properties that are examined for both solutions and films include UV–vis absorption spectroscopy, photoluminescence spectroscopy, and red‐edge optical band gaps. All the properties studied are strongly dependent both on protonation state and percentage of carboxylic acid/carboxylate side chains along the polymer backbone. The anionic form of each polythiophene derivative was also used in layer‐by‐layer film deposition with a cationic phosphonium polyelectrolyte. The film growth process was studied by spectroscopic techniques to assess the influence of side‐chain composition on the film growth and optical properties. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem.2019

 

The properties of phosphonium polyelectrolytes (PELs) were evaluated in an effort to assess the influence of both side chain and main chain composition. The influence of side chain was examined by comparing properties of a series of PELs having hydrophobic octyloxy side chains to those of structural analogues lacking the side chains. The influence exerted by backbone flexibility/length of spacer between charges was revealed by comparing properties of two series of polymers with a variable number of methylene units between phosphonium charge‐bearing sites. Side chain composition and spacing between phosphonium units lead to noteworthy influence on thermal stability, glass transition, and crystallinity. The molecular structure of PELs also correlates with trends in film morphology and critical surface energy of PEL dip‐cast films. Sensitivity of morphology to humidity or water in the casting solvent was observed. Supramolecular assembly of films via layer‐by‐layer deposition of PELs alternating with anionic polythiophene derivative layers was also undertaken. The linearity of film growth, amount of material deposited in each bilayer, polycation:polyanion ratio, and film roughness all show noteworthy trends that depend on both the presence/absence of side chains and on spacing between ionic centers. The relationship between side chain and spacer on bactericidal activity againstStaphylococcus aureusandEscherichia coliwas assessed. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem.2019, 57, 24–34