Miracle-dz/UniFAR-FTUs huggingface dataset:
Form and function of the condensed bacterial nucleoid studied by cryo-ET

All cells share a common genetic code. From the nucleus of mammalian cells to the nucleoid of the smallest bacterium, all cells use DNA to encode the traits and cellular processes that drive the diversity of life on earth. Replication of the genome is the most fundamental form of heritability and errors or damage during this process can have dire consequences. It is therefore of great importance that all organisms carry out mechanisms to protect nuclear material in the event of cellular stress. To study the form and function of DNA structures during cellular stress, we are using cryo-electron tomography (cryo-ET) of bacteria. In doing this, we use the bacterial nucleoid as a reporter of in vivo chromosome structure, and to study the replication and protection of a cells’ genetic material. For this work we are using Rhodobactersphaeroides, a facultative alphaproteobacterium with features that make it attractive for analysis. R. sphaeroides cultures were grown at 30°C and 200 RPM shaking in Sistrom’s minimal medium until the culture reached an OD 600 of 0.4. Cells were then treated with the antibiotic chloramphenicol or transferred to ice to produce cold shock. Suspensions of the cells were deposited onto 200 mesh R2/1 copper Quantifoil grids in 5 μL aliquots, blotted, and plunge frozen in liquid ethane using a Vitrobot Mark IV (ThermoScientific). Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) data were collected using a Titan Krios TEM (ThermoScientific)
Development and Deployment of Automated Machine Learning Detection in Electron Microcopy Experiments

Electron microscopy (EM) is only a decade away from reaching its centennial birthday and, during its maturation, has become the de facto method for evaluating materials evolution for a range of environments. The look, feel, and operation of electron microscopes have changed significantly since the birth of the field, but the way we analyze the images generated from EM has only come from one generational leap – digitization. Digitization has eliminated the bottleneck associated with the critical activity of generating, organizing, and storing electron images, but one still remains after nearly ninety years: identification, classification, and quantification of the features present in an analog or digital EM image. The EM community has identified this issue and is now leveraging modern, automated feature detection algorithms, such as those built on large, deep convolution neural networks (CNNs) [1–9]. The use of CNNs may provide for a second generational leap – automation. Ultimately, issues have arisen limiting the community's ability to arrive at this leap. The first issue is the lack of substantial, unbiased ground truth labeled images for training CNNs, a second is developing and vetting CNNs for real-time detection, and a third issue is deploying the trained CNNs at the point-of-acquisition in an easy to use and consumable digital format. Here, we demonstrate on-going pursuits toward providing automation in electron microscopy images using CNNs. The first pursuit is the application of a lightweight, reduced ordered transmission electron microscopy image simulation method to generate large amounts of "synthetic" data for training common CNN architectures. Specifically, we have developed a Python-based image generation algorithm that uses simplified phase-contrast models to simulate features of interest in either an under- or over-focused
Imaging of localized surface plasmonic field at nanoscale by UEM

The Ultrafast Electron Microscopy (UEM) is one powerful tool to study the ultrafast dynamics of light-matter interaction at a high temporal resolution of sub-picosecond time scale that is 10 orders better than the conventional TEM. [1] The UEM has been widely used in physics, chemistry and materials science. Other than light-matter interaction observed in UEM, the investigation of photon-electron interaction has been enabled by one unique technique called Photon-Induced Near Field Electron Microscopy (PINEM) developed in UEM to capture the evanescent electromagnetic field on its intrinsic time scale and nanometer scale. [2, 3] In free space, the photon-electron interaction is forbidden due to the energy-momentum mismatch. However, the energy-momentum mismatch can be eliminated through the altered dispersion relation of photons using materials. In PINEM, the free electrons gain or loss quantized photon energy when electrons overlap with photons spatiotemporally near the nanostructures or interfaces, where the phase velocity of photons is reduced to the group velocity of free electrons due to the spatial confinement. The images formed by those electrons gaining photon energy are called PINEM image for plasmonic field mapping in UEM. Surface plasmons, collective oscillating charge wave at the surface of metals or interface of metals/dielectrics under light excitation, are among the most attractive candidates for next-generation information due their extremely and strong coupling The
Maximum thicknesses of EELS log ratio thickness measurement for several elements

Sample thickness ( t ) projected along the electron beam path can be measured in a transmission electron microscope (TEM) equipped with an energy filter for example by the log-ratio method. The relation underlying the log-ratio method is here t , λ , I and I 0 are sample thickness, inelastic mean free path (IMFP), total intensity of the incident beam and zero-loss filtered intensity [1, 2]. Eq. (1) can be applied to a pair of unfiltered and filtered images pixel by pixel providing a two-dimensional sample thickness map. We investigate the upper limit of the method when applied to thick samples. In particular, we are concerned with the maximum thickness yielding a linear dependence of ln( I / I 0 ) on t . The
Symmetry Analysis in Metallic Glasses by Electron Nanodiffraction

Rotational symmetries in electron nanodiffraction (END) measured by 4D STEM can reveal and map local structure in metallic glasses [1][2]. The angular autocorrelation function and its power spectrum have been used to characterize local symmetries, but we show that it can exhibit several types of artifacts. We have adapted the Symmetry STEM method, recently proposed to probe local symmetry in 4D STEM data from crystals [3], to analyze rotational symmetry in 4D STEM data from metallic glasses without artifacts. into polar coordinates in periodic symmetry Fourier to analysis. ,. ,Δ
Exploring in-situ viral infection with multi-modal cryogenic correlative FLM-FIB/SEM/Cryo-ET for vitrified mammalian cells

Cryo-electron tomography (cryo-ET) has emerged as a powerful tool to provide in-situ detailed, nanometer to sub-nanometer level three-dimensional (3D) information on cellular ultrastructure and macromolecular complexes during host-virus interactions [1]. However, a few challenges remain when using cryo-ET for in situ structural studies, that include difficulties in locating and identifying features of interest within complex cellular surroundings and the thickness of some samples. Electrons are prone to inelastic scattering in biological samples thicker than 300 nm, e.g., regions away from thin cell periphery of eukaryotic cells, this results in image blurring and decreased resolution. To visualize them, cryo-focused ion beam (FIB) milling coupled with scanning electron microscopy (cryo-FIB/SEM) has been used to generate thin lamella from vitrified eukaryotic cells, making a wide variety of exciting biological events accessible for cryo-ET [2]. Cryo-correlative light and electron microscopy (cryo-CLEM), the coupling of fluorescent light microscopy (FLM) and transmission electron microscopy (TEM), adds temporal and spatial information from FLM to cryo-ET, facilitating identification of rare or dynamic events during viral infection [3]. Thus, multi-modal and -scale microscopy under cryo-conditions has been explored to locate fluorescent regions of interest by cryo-FLM, in a thin lamella of the vitrified eukaryotic cell produced by cryo-FIB/SEM,
Electron beam effects in high-resolution transmission electron microscopy investigations of catalytic nanoparticles

High-resolution transmission electron microscopy (HRTEM) is a powerful tool for atomic scale investigations of catalytic nanoparticles. The dynamics of such catalytic nanoparticles are highly dependent on the environment: temperature, reactant gases and reactor pressure. It is possible to imitate such conditions in a transmission electron microscope (TEM). Still, major gaps remain between HRTEM investigations and industrial reactors [1].
Homology model of Drosophila melanogaster myosin filaments

Striated muscle is made of muscle cells or fibers, consisting of hundreds of myofibrils which are usually about 1 to 2 µm in diameter. Myofibrils are composed of repeating units called sarcomeres which are arranged in tandem for the entire length of the muscle and are responsible for generating tension during muscle contraction. Sarcomeres are composed of four basic components: bipolar, myosin-containing thick filaments; polar, actin-containing thin filaments; a Z-disk which cross-links antiparallel actin filaments into a bipolar structure; and a connecting filament to link the thick filaments to the Z-disk. Muscle contraction and force generation is caused by interactions between myosin heads (thick filaments) and actin filaments (thin filaments). Myosin consists of a head which contains the molecular motor and a long, 1600 Å a-helical coiled coil tail which comprises much of the mass of the thick filament backbone. Myosin’s tail is its least understood domain. Drosophila melanogaster , the fruit fly, is a highly used genetic model organism in the study of human disease. human Drosophila The , ,
4-D STEM Analyses of Cylindrical Specimens for Atom Probe Tomography

Atom Probe Tomography (APT) data reconstruction using geometrical models of field evaporation has many limitations due to the number of free empirical values. Correlating information from Transmission Electron Microscopy (TEM) and APT on the same specimen has shown the ability to determine the specimen volume using pre- and post-APT imaging, thus limiting the free variables. Additionally, a recent method using TEM and electron diffraction data has made it possible to incorporate atomic positions within the APT data reconstruction volumes [1], a method dubbed STEM-centric atom placement [2]. In this work, we expand upon the idea of STEM-centric atom placement by utilizing 4-D STEM data, where convergent beam electron diffraction data is collected at sub-nm spacings across the cylindrical atom probe specimen before APT analysis is conducted. In this work, a SiGe metamorphic buffer layer on a Si (100) substrate was grown using molecular beam epitaxy and prepared for STEM and APT using standard focused ion beam (FIB) methods. The sample was prepared on Au TEM grids and followed by 5 kV cleaning steps. 4-D STEM analyses were conducted using a Thermo Fisher Scientific Talos F200X STEM at 200 keV and a Ceta camera at 512 x 512 pixel density. Bright field, dark field, and differential phase contrast micrographs were produced from the data using LiberTEM [3]. Strain analyses were conducted using Exit Wave Phase Cepstrum (EWPC) methods [4]. APT data was collected on a Cameca LEAP 4000 XSi using 25 pJ of laser energy per pulse and a base temperature of 40 K. Figure 1 illustrates the ability of 4-D STEM to identify the atomic structure within the cylindrical specimens. Crystallographic phase and orientations can be determined
Combined chemical and structural analysis of low dimensional systems in FEG-SEM

Correlative microscopy is a powerful analytical approach combining strengths of different techniques for study of materials. In this study we present simultaneous chemical and structural analysis of FIB-lamella extracted from Cu(In,Ga)Se2 (CIGS) solar cells in SEM. Chemical analysis of low-dimensional systems (nanoparticles, nanorods, TEM lamellae, etc.) in SEM highly desirable of and for sample preparation. to and other the solid which detectors a the of yield which complicates reliable chemical identification. this analysis of e-transparent samples in SEM challenging.
The crystal structure of the meteoritic mineral krinovite, NaMg2CrSi3O10

Krinovite, a meteoritic silicate mineral with ideal crystal chemical formula Na 2 Mg 4 Cr 2 [S i 6 0 1 8 ]0 2 , is triclinic, space group PT, with a = 10.238(4), b = 10.642(4), c = 8.780(3) Â, α = 105.15(3)°, β = 96.50(4)°, y = 125.15(3)°. The crystal structure of krinovite has been refined to R = 0.065, R v = 0.067 for 1661 independent reflections. Krinovite is a member of the aenigmatite group, and is characterized by octahedral walls connected through tetrahedral chains with appendices and additional insular octahedra.
Analysis of MnFe2O4 phase transition induced by the energy of electron beam in an iron-manganese oxide nanoparticle

The iron-manganese oxide (FeMn-ox) is a material that has carried great interest in recent years because of the promising application in environmental remediation, hydrogen production, and catalytic degradation of organic pollutants [1-3]. Since the strong relationship of structure and properties was found in the crystalline materials, the analysis of in-situ changes of the structure has carried attention due to constitute a useful tool for developing formation mechanisms [4, 5]. In addition, it is known that the energy content in the electron beam of the transmission electron microscope (TEM) can promote repositioning of the atomic species and structural changes in metal oxide nanoparticles [6]. Therefore, the beam energy can be used to induce a phase transformation of FeMn-ox nanoparticles. In this work, iron-manganese oxide (FeMn-ox) nanoparticles were synthesized by the hydrothermal method. Four aqueous solutions were required: sodium dodecyl sulfate (0.15 M), sodium hydroxide (2.5 M), iron (III) chloride (0.3 M), and manganese chloride (0.3 M). The solutions were mixed, and a total volume of 25 mL was introduced into an autoclave for heating the entire system at 80 ⁰ C, under an isothermal process, for 1h. Afterward, the sample was cleaned in an ultrasonic bath of deionized water. The characterizations were focused to study the phase transition in a single FeMn-ox nanoparticle by transmission
Automated Data Labeling and Label Cleaning for Nanoparticle Classification in Electron Microscopy

While there have been a large number of advances for rapid data acquisition in electron microscopy, gaining meaningful insights from this data remains challenging. This is often due to the challenges in creating a robust way to analyze the large amount of data generated. Manually labeling large datasets is often too time consuming, while automated techniques are more error-prone. However, recent advances in the machine learning community have focused on how to develop accurate classifiers from datasets with erroneous labels like those generated by automated techniques, yielding neural networks which are able to accurately analyze the data without the time investment from researchers [1-7]. Here, we analyze two approaches to outperforming accuracy limits imposed by label noise: noise robust techniques, which are immune to erroneous labels, and noise cleaning techniques, network training procedures which work to remove erroneous examples. The case we use to test these methods is classifying handedness of chiral nanoparticles from scanning electron microscopy (SEM) data [8]. This dataset provides a good test case in which data labeling can be easily automated but is likely to have a certain fraction of erroneous labels. Synthesis routes for chiral particles often lead to skew in the population to one handedness or the other, to create labels we can assume all one handedness and mirror the images to create the opposite handedness. To control for the error rate we use a set of single handed nanoparticles and synthetically introduce error, as shown in Figure 1. In this way we create an electron microscopy dataset with controlled error rates to test how methods respond across varying error rates. Using this dataset, we study which methods yield the best performance. We will first analyze the basic noise robustness of convolutional neural networks (CNNs) [2] to the binary label flip noise and the limits of relying on this robustness for network development. We will then discuss a two step training procedure in which we train basic CNNs initially with an extremely small clean dataset [7], before introducing noise, leading to a more noise robust network. Finally, we test a co-teaching method, developed by Han et al [5], which uses two networks trained in tandem to try and filter erroneous labels while training. We find that the two step training procedure leads to the highest accuracy networks and is robust to noise at higher error rates than other methods. Finally, we demonstrate these methods as part of a complete analysis pipeline, automating particle segmentation and handedness classification for large SEM datasets of chiral Te nanoparticles.
Identifying Individual Atoms in Single Atom Pt/CeO2 Catalysts

Single-atom catalysts have recently attracted a lot of attention because of their unique reactivity and atom efficiency, which becomes very important for scarce platinum group metals. Heterogeneous catalysts consist of nanoparticles on oxide supports. Only a fraction of the total number of atoms in a nanoparticle are located on the surface, hence those atoms in the bulk are wasted since they do not participate in the catalysis occurring at the surface. If the precious metal is atomically dispersed on a support, every atom can participate in the chemical reaction. Aberration corrected STEM imaging has been responsible, in part, for the growing interest in this field, since these isolated metal atoms can now be imaged [1].
Tar Conversion and Vapor Upgrading via in Situ Catalysis Using Silica-Based Nickel Nanoparticles Embedded in Rice Husk Char for Biomass Pyrolysis/Gasification

This paper proposes two simple approaches for in situ generation of nickel nanoparticles in the carbon matrix of rice husk char (RHC) via incipient wetness impregnation and pyrolysis. The nickel oxides (i.e., NiO) in the RHC matrix can be preliminarily in situ reduced into metallic nickel (Ni0) by carbothermal reduction. Furthermore, due to the strong reducibility, the addition of NaBH4 could significantly promote the formation of crystalline metallic nickel (Ni0) nanoparticles. Interestingly, NaBH4 added into the Ni2+ solution can generate NaOH after catalytic hydrolysis, which contributed to the increase of BET surface area and the change of char pore structures by wet activation. More importantly, RHC Ni catalysts exhibited good performances on catalytic tar conversion with syngas upgrading during copyrolysis with biomass, especially in terms of tar conversion efficiency up to 96.5%. Consequently, in situ catalytic conversion could inhibit the micromolecular nascent tar polymerization, thereby reducing.
Lack of the Bacterial Phytochrome Protein Decreases Deinococcus radiodurans Resistance to Mitomycin C

Deinococcus radiodurans known for its extraordinary resistance to ionizing radiation contains bacterial phytochrome (BphP), a member of the family of red/far-red light-sensing proteins. In this study, we constructed a bphP mutant strain (ΔbphP) to investigate the role of D. radiodurans BphP (DrBphP) in the DNA damage response. When cells were incubated under light and dark conditions following exposure to DNA damaging agents, such as γ- and UV-radiation and mitomycin C (MMC), no significant difference in cell survival was observed between the wild-type D. radiodurans strain (WT) and ΔbphP. However, when continuously exposed to MMC under light conditions, the WT strain notably exhibited increased survival compared to cells grown in the dark. The increased survival was not observed in the ΔbphP strain. These results are indicative of the protective role of light-activated DrBphP in the presence of MMC. Site-directed mutagenesis revealed that the conserved amino acids Cys-24 and His-532 involved in chromophore binding and signal transduction, respectively, were essential for the protective function of DrBphP. Inactivation of the cognate response regulator (RR; DrBphR) of DrBphP increased MMC resistance in the dark. In trans complementation of the bphP bphR double mutant strain (ΔbphPR) with DrBphR decreased MMC resistance. Considering that DrBphP acts as a light-activated phosphatase that dephosphorylates DrBphR, it appears that phosphorylated DrBphR exerts a negative effect on cell survival in the presence of MMC. DrBphP overexpression resulted in an increase in MMC resistance of ΔbphPR, implying that other RRs might be involved in the DrBphP-mediated signaling pathway. A mutant lacking the dr_0781 gene (Δdr_0781) demonstrated the same MMC phenotype as ΔbphR. Survival was further increased in the bphR dr_0781 double mutant strain compared to each single mutant ΔbphR or Δdr_0781, suggesting that DR_0781 is also involved in the DrBphP-dependent MMC sensitivity. This study uncovered a previously unknown phenomenon of red/far-red light-dependent DNA damage survival mediated by BphP by identifying the conditions under which DrBphP exhibits a fitness advantage.
Parental Social Comparison Related to Tutoring Anxiety, and Guided Approaches to Assisting Their Children's Home Online Learning During the COVID-19 Lockdown

The COVID-19 pandemic has caused great disruption in education systems around the world. Schools have in some cases ended or limited on-site teaching, and have shifted toward home online teaching. This situation is likely to cause increased uncertainty and anxiety for parents who on one hand may question the quality of home online learning yet, on the other, may not feel sufficiently confident or competent to guide their children's home online learning. Resulting anxiety is expected to be most evident in competitive educational contexts, such as those found throughout much of East Asia. Therefore, using China as the setting, and social comparison theory as the framework, this study examined how variation in parent social comparison relates to parent tutoring anxiety and, in turn, to the types of guided strategies parents use to promote their children's home online learning. The results indicated a positive relationship between parental upward social comparison and parental tutoring anxiety, but a negative relationship between parent downward social comparison and parental anxiety. Parental tutoring anxiety is positively related to the confirmation and structure types, but negatively related to the discovery type of guided approaches. The implication of this study is that parents who resist tendencies of competitive upward social comparison are likely to adopt more effective approaches to guiding their children's home online learning.
Interspecific Niche Competition Increases Morphological Diversity in Multi-Species Microbial Communities

Intraspecific competition for limited niches has been recognized as a driving force for adaptive radiation, but results for the role of interspecific competition have been mixed. Here, we report the adaptive diversification of the model bacteria Pseudomonas fluorescens in the presence of different numbers and combinations of four competing bacterial species. Increasing the diversity of competitive community increased the morphological diversity of focal species, which is caused by impeding the domination of a single morphotype. Specifically, this pattern was driven by more diverse communities being more likely to contain key species that occupy the same niche as otherwise competitively superior morphotype, and thus preventing competitive exclusion within the focal species. Our results suggest that sympatric adaptive radiation is driven by the presence or absence of niche-specific competitors.