Effect of Sample Geometry on Graphitization of Polyacrylonitrile.

In this study, it is analyzed how sample geometry (spheres, nanofibers, or films) influences the graphitization behavior of polyacrylonitrile (PAN) molecules. The chemical bonding and changes in the composition of these three geometries are studied at the oxidation, carbonization, and graphitization stages via scanning electron microscopy (SEM), in situ thermogravimetric-infrared (TGA-IR) analysis, elemental analysis, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). The influence of molecular alignment on the graphitization of the three sample geometries is investigated using synchrotron wide-angle X-ray diffraction (WAXD) and transmission electron microscopy (TEM). The effects of molecular alignment at different draw rates during spinning are explored in detail.

Plant-Derived Anti-Human Epidermal Growth Factor Receptor 2 Antibody Suppresses Trastuzumab-Resistant Breast Cancer with Enhanced Nanoscale Binding.

Traditional monoclonal antibodies such as Trastuzumab encounter limitations when treating Human Epidermal Growth Factor Receptor 2 (HER2)-positive breast cancer, particularly in cases that develop resistance. This study introduces plant-derived anti-HER2 variable fragments of camelid heavy chain domain (VHH) fragment crystallizable region (Fc) KEDL(K) antibody as a potent alternative for overcoming these limitations. A variety of biophysical techniques, in vitro assays, and in vivo experiments uncover the antibody's nanoscale binding dynamics with transmembrane HER2 on living cells. Single-molecule force spectroscopy reveals the rapid formation of two robust bonds, exhibiting approximately 50 pN force resistance and bond lifetimes in the second range. The antibody demonstrates a specific affinity for HER2-positive breast cancer cells, including those that are Trastuzumab-resistant. Moreover, in immune-deficient mice, the plant-derived anti-HER2 VHH-FcK antibody exhibits superior antitumor activity, especially against tumors that are resistant to Trastuzumab. These findings underscore the plant-derived antibody's potential as an impactful immunotherapeutic strategy for treating Trastuzumab-resistant HER2-positive breast cancer.

Organ-on-a-Chip for Studying Gut-Brain Interaction Mediated by Extracellular Vesicles in the Gut Microenvironment.

Extracellular vesicles (EVs) are a group of membrane vesicles that play important roles in cell-to-cell and interspecies/interkingdom communications by modulating the pathophysiological conditions of recipient cells. Recent evidence has implied their potential roles in the gut-brain axis (GBA), which is a complex bidirectional communication system between the gut environment and brain pathophysiology. Despite the evidence, the roles of EVs in the gut microenvironment in the GBA are less highlighted. Moreover, there are critical challenges in the current GBA models and analyzing techniques for EVs, which may hinder the research. Currently, advances in organ-on-a-chip (OOC) technologies have provided a promising solution. Here, we review the potential effects of EVs occurring in the gut environment on brain physiology and behavior and discuss how to apply OOCs to research the GBA mediated by EVs in the gut microenvironment.

Gut-Kidney Axis on Chip for Studying Effects of Antibiotics on Risk of Hemolytic Uremic Syndrome by Shiga Toxin-Producing Escherichia coli.

Shiga toxin-producing Escherichia coli (STEC) infects humans by colonizing the large intestine, and causes kidney damage by secreting Shiga toxins (Stxs). The increased secretion of Shiga toxin 2 (Stx2) by some antibiotics, such as ciprofloxacin (CIP), increases the risk of hemolytic-uremic syndrome (HUS), which can be life-threatening. However, previous studies evaluating this relationship have been conflicting, owing to the low frequency of EHEC infection, very small number of patients, and lack of an appropriate animal model. In this study, we developed gut-kidney axis (GKA) on chip for co-culturing gut (Caco-2) and kidney (HKC-8) cells, and observed both STEC O157:H7 (O157) infection and Stx intoxication in the gut and kidney cells on the chip, respectively. Without any antibiotic treatment, O157 killed both gut and kidney cells in GKA on the chip. CIP treatment reduced O157 infection in the gut cells, but increased Stx2-induced damage in the kidney cells, whereas the gentamycin treatment reduced both O157 infection in the gut cells and Stx2-induced damage in the kidney cells. This is the first report to recapitulate a clinically relevant situation, i.e., that CIP treatment causes more damage than gentamicin treatment. These results suggest that GKA on chip is very useful for simultaneous observation of O157 infections and Stx2 poisoning in gut and kidney cells, making it suitable for studying the effects of antibiotics on the risk of HUS.

A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C.

Chromosomes are giant chain molecules organized into an ensemble of three-dimensional structures characterized with its genomic state and the corresponding biological functions. Despite the strong cell-to-cell heterogeneity, the cell-type specific pattern demonstrated in high-throughput chromosome conformation capture (Hi-C) data hints at a valuable link between structure and function, which makes inference of chromatin domains (CDs) from the pattern of Hi-C a central problem in genome research. Here we present a unified method for analyzing Hi-C data to determine spatial organization of CDs over multiple genomic scales. By applying statistical physics-based clustering analysis to a polymer physics model of the chromosome, our method identifies the CDs that best represent the global pattern of correlation manifested in Hi-C. The multi-scale intra-chromosomal structures compared across different cell types uncover the principles underlying the multi-scale organization of chromatin chain: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by different organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. Our physically principled interpretation and analysis of Hi-C not only offer an accurate and quantitative view of multi-scale chromatin organization but also help decipher its connections with genome function.

Heterogeneous Loop Model to Infer 3D Chromosome Structures from Hi-C.

Adapting a well-established formalism in polymer physics, we develop a minimalist approach to infer three-dimensional folding of chromatin from Hi-C data. The three-dimensional chromosome structures generated from our heterogeneous loop model (HLM) are used to visualize chromosome organizations that can substantiate the measurements from fluorescence in situ hybridization, chromatin interaction analysis by paired-end tag sequencing, and RNA-seq signals. We demonstrate the utility of the HLM with several case studies. Specifically, the HLM-generated chromosome structures, which reproduce the spatial distribution of topologically associated domains from fluorescence in situ hybridization measurement, show the phase segregation between two types of topologically associated domains explicitly. We discuss the origin of cell-type-dependent gene-expression level by modeling the chromatin globules of α-globin and SOX2 gene loci for two different cell lines. We also use the HLM to discuss how the chromatin folding and gene-expression level of Pax6 loci, associated with mouse neural development, are modulated by interactions with two enhancers. Finally, HLM-generated structures of chromosome 19 of mouse embryonic stem cells, based on single-cell Hi-C data collected over each cell-cycle phase, visualize changes in chromosome conformation along the cell-cycle. Given a contact frequency map between chromatic loci supplied from Hi-C, HLM is a computationally efficient and versatile modeling tool to generate chromosome structures that can complement interpreting other experimental data.

Investigation of Composite Nano Air Filter for Improving Antimicrobial Activity and Reducing VOCs Using a High Speed Upward Electrospinning System.

A new nano air filter for fine dust filtration with antibacterial and volatile organic compounds (VOCs) adsorption properties was fabricated using a bottom-up, high-speed electrospinning system. To optimize production, polyurethane fibers were electrospun at various voltages on polypropylene nonwoven fabrics, and results show that fiber diameter decreased as voltage increased. Silver nanoparticles (AgNPs) and Activated Carbon (AC) were used as antimicrobials and VOC-reducing agents. FTIR, SEM, and EDS were performed to analyze the resulting filter fabricated by electrospinning. FTIR and EDS results show that the AgNPs and activated carbon added to the PU fibers were successfully integrated into the PP nonwoven fabric.

Dynamic Transcriptome, DNA Methylome, and DNA Hydroxymethylome Networks During T-Cell Lineage Commitment.

The stepwise development of T cells from a multipotent precursor is guided by diverse mechanisms, including interactions among lineage-specific transcription factors (TFs) and epigenetic changes, such as DNA methylation and hydroxymethylation, which play crucial roles in mammalian development and lineage commitment. To elucidate the transcriptional networks and epigenetic mechanisms underlying T-cell lineage commitment, we investigated genome-wide changes in gene expression, DNA methylation and hydroxymethylation among populations representing five successive stages of T-cell development (DN3, DN4, DP, CD4+, and CD8+) by performing RNA-seq, MBD-seq and hMeDIP-seq, respectively. The most significant changes in the transcriptomes and epigenomes occurred during the DN4 to DP transition. During the DP stage, many genes involved in chromatin modification were up-regulated and exhibited dramatic changes in DNA hydroxymethylation. We also observed 436 alternative splicing events, and approximately 57% (252) of these events occurred during the DP stage. Many stage-specific, differentially methylated regions were observed near the stage-specific, differentially expressed genes. The dynamic changes in DNA methylation and hydroxymethylation were associated with the recruitment of stage-specific TFs. We elucidated interactive networks comprising TFs, chromatin modifiers, and DNA methylation and hope that this study provides a framework for the understanding of the molecular networks underlying T-cell lineage commitment.

An Impedance-Transduced Chemiresistor with a Porous Carbon Channel for Rapid, Nonenzymatic, Glucose Sensing.

A new type of chemiresistor, the impedance-transduced chemiresistor (ITCR), is described for the rapid analysis of glucose. The ITCR exploits porous, high surface area, fluorine-doped carbon nanofibers prepared by electrospinning of fluorinated polymer nanofibers followed by pyrolysis. These nanofibers are functionalized with a boronic acid receptor and stabilized by Nafion to form the ITCR channel for glucose detection. The recognition and binding of glucose by the ITCR is detected by measuring its electrical impedance at a single frequency. The analysis frequency is selected by measuring the signal-to-noise ( S/ N) for glucose detection across 5 orders of magnitude, evaluating both the imaginary and real components of the complex impedance. On the basis of this analysis, an optimal frequency of 13 kHz is selected for glucose detection, yielding an S/ N ratio of 60-100 for [glucose] = 5 mM using the change in the total impedance, Δ Z. The resulting ITCR glucose sensor shows a rapid analysis time (<8 s), low coefficient of variation for a series of sensors (<10%), an analysis range of 50 µM to 5 mM, and excellent specificity versus fructose, ascorbic acid, and uric acid. These metrics for the ITCR are obtained using a sample size as small as 5 µL.

Bimodally Porous WO3 Microbelts Functionalized with Pt Catalysts for Selective H2S Sensors.

Bimodally meso- (2-50 nm) and macroporous (>50 nm) WO3 microbelts (MBs) functionalized with sub-3 nm Pt catalysts were fabricated via the electrospinning technique followed by subsequent calcination. Importantly, apoferritin (Apo), tea saponin and polystyrene colloid spheres (750 nm) dispersed in an electrospinning solution acted as forming agents for producing meso- and macropores on WO3 MBs during calcination. Particularly, mesopores provide not only numerous reaction sites for effective chemical reactions, but also facilitate gas diffusion into the interior of the WO3 MBs, dominated by Knudsen diffusion. The macropores further accelerate gas permeability in the interior and on the exterior of the WO3 MBs. In addition, Pt nanoparticles with mean diameters of 2.27 nm were synthesized by using biological protein cages, such as Apo, to further enhance the gas sensing performance. Bimodally porous WO3 MBs functionalized by Pt catalysts showed remarkably high hydrogen sulfide (H2S) response ( Rair/ Rgas = 61 @ 1 ppm) and superior selectivity to H2S against other interfering gases, such as acetone (CH3COCH3), ethanol (C2H5OH), ammonia (NH3), and carbon monoxide (CO). These results demonstrate a high potential for the feasibility of catalyst-loaded meso- and macroporous WO3 MBs as new sensing platforms for the possibility of real-time diagnosis of halitosis.

Bioinspired Cocatalysts Decorated WO3 Nanotube Toward Unparalleled Hydrogen Sulfide Chemiresistor.

Herein, we incorporated dual biotemplates, i.e., cellulose nanocrystals (CNC) and apoferritin, into electrospinning solution to achieve three distinct benefits, i.e., (i) facile synthesis of a WO3 nanotube by utilizing the self-agglomerating nature of CNC in the core of as-spun nanofibers, (ii) effective sensitization by partial phase transition from WO3 to Na2W4O13 induced by interaction between sodium-doped CNC and WO3 during calcination, and (iii) uniform functionalization with monodispersive apoferritin-derived Pt catalytic nanoparticles (2.22 ± 0.42 nm). Interestingly, the sensitization effect of Na2W4O13 on WO3 resulted in highly selective H2S sensing characteristics against seven different interfering molecules. Furthermore, synergistic effects with a bioinspired Pt catalyst induced a remarkably enhanced H2S response ( Rair/ Rgas = 203.5), unparalleled selectivity ( Rair/ Rgas < 1.3 for the interfering molecules), and rapid response (<10 s)/recovery (<30 s) time at 1 ppm of H2S under 95% relative humidity level. This work paves the way for a new class of cosensitization routes to overcome critical shortcomings of SMO-based chemical sensors, thus providing a potential platform for diagnosis of halitosis.

Nitrogen-Doped Single Graphene Fiber with Platinum Water Dissociation Catalyst for Wearable Humidity Sensor.

Humidity sensors are essential components in wearable electronics for monitoring of environmental condition and physical state. In this work, a unique humidity sensing layer composed of nitrogen-doped reduced graphene oxide (nRGO) fiber on colorless polyimide film is proposed. Ultralong graphene oxide (GO) fibers are synthesized by solution assembly of large GO sheets assisted by lyotropic liquid crystal behavior. Chemical modification by nitrogen-doping is carried out under thermal annealing in H2 (4%)/N2 (96%) ambient to obtain highly conductive nRGO fiber. Very small (≈2 nm) Pt nanoparticles are tightly anchored on the surface of the nRGO fiber as water dissociation catalysts by an optical sintering process. As a result, nRGO fiber can effectively detect wide humidity levels in the range of 6.1-66.4% relative humidity (RH). Furthermore, a 1.36-fold higher sensitivity (4.51%) at 66.4% RH is achieved using a Pt functionalized nRGO fiber (i.e., Pt-nRGO fiber) compared with the sensitivity (3.53% at 66.4% RH) of pure nRGO fiber. Real-time and portable humidity sensing characteristics are successfully demonstrated toward exhaled breath using Pt-nRGO fiber integrated on a portable sensing module. The Pt-nRGO fiber with high sensitivity and wide range of humidity detection levels offers a new sensing platform for wearable humidity sensors.

Development of Nanofiber Reinforced Double Layered Cabin Air Filter Using Novel Upward Mass Production Electrospinning Set Up.

Recently, numerous researchers are interested in the development of new air filter because of air pollution caused by rapid industrialization and urbanization. The major concerns in developing air filters are: pressure drop and filtration efficiency which are considered significant. As the pressure drop increases, the energy consumption becomes high. In this study, we developed a novel air filter (polyurethane fiber mat) for nano size filtration using a mass production electrospinning, which is expected to enhance filtration efficiency and pressure drop effects. To determine the optimal electrospinning conditions for filter efficiency, various concentrations (8, 10, 12 wt/wt%) of thermoplastic polyurethane were prepared and employed. Scanning electron microscope (SEM) and Fourier transform infrared spectroscopy (FT-IR) were used for fiber characterization, and finally, efficiency test was conducted to evaluate the filter performance of developed nanofiber-based air filter. From this study, it could be concluded that optimization by adjusting the polymer concentration and electrospinning operating condition was the best efficient alternative method to fabricate nano-fibrous air filter system with improved filtration performance.

Normal mode analysis of Zika virus.

In recent years, Zika virus (ZIKV) caused a new pandemic due to its rapid spread and close relationship with microcephaly. As a result, ZIKV has become an obvious global health concern. Information about the fundamental viral features or the biological process of infection remains limited, despite considerable efforts. Meanwhile, the icosahedral shell structure of the mature ZIKV was recently revealed by cryo-electron microscopy. This structural information enabled us to simulate ZIKV. In this study, we analyzed the dynamic properties of ZIKV through simulation from the mechanical viewpoint. We performed normal mode analysis (NMA) for a dimeric structure of ZIKV consisting of the envelope proteins and the membrane proteins as a unit structure. By analyzing low-frequency normal modes, we captured intrinsic vibrational motions and defined basic vibrational properties of the unit structure. Moreover, we also simulated the entire shell structure of ZIKV at the reduced computational cost, similar to the case of the unit structure, by utilizing its icosahedral symmetry. From the NMA results, we can not only comprehend the putative dynamic fluctuations of ZIKV but also verify previous inference such that highly mobile glycosylation sites would play an important role in ZIKV. Consequently, this theoretical study is expected to give us an insight on the underlying biological functions and infection mechanism of ZIKV.

Nanoscale PtO2 Catalysts-Loaded SnO2 Multichannel Nanofibers toward Highly Sensitive Acetone Sensor.

PtO2 nanocatalysts-loaded SnO2 multichannel nanofibers (PtO2-SnO2 MCNFs) were synthesized by single-spinneret electrospinning combined with apoferritin and two immiscible polymers, i.e., poly(vinylpyrrolidone) and polyacrylonitrile. The apoferritin, which can encapsulate nanoparticles within a small inner cavity (8 nm), was used as a catalyst loading template for an effective functionalization of the PtO2 catalysts. Taking advantage of the multichannel structure with a high porosity, effective activation of catalysts on both interior and exterior site of MCNFs was realized. As a result, under high humidity condition (95% RH), PtO2-SnO2 MCNFs exhibited a remarkably high acetone response (Rair/Rgas = 194.15) toward 5 ppm acetone gases, superior selectivity to acetone molecules among various interfering gas species, and excellent stability during 30 cycles of response and recovery toward 1 ppm acetone gases. In this work, we first demonstrate the high suitability of multichannel semiconducting metal oxides structure functionalized by apoferritin-encapsulated catalytic nanoparticles as highly sensitive and selective gas-sensing layer.

Dynamic characteristics of a flagellar motor protein analyzed using an elastic network model.

At the base of a flagellar motor, its rotational direction and speed are regulated by the interaction between rotor and stator proteins. A switching event occurs when the cytoplasmic rotor protein, called C-ring, changes its conformation in response to binding of the CheY signal protein. The C-ring structure consists of FliG, FliM, and FliN proteins and its conformational changes in FliM and FliG including HelixMC play an important role in switching the motor direction. Therefore, clarifying their dynamic properties as well as conformational changes is a key to understanding the switching mechanism of the motor protein. In this study, to elucidate dynamic characteristics of the C-ring structure, both harmonic (intrinsic vibration) and anharmonic (transition pathway) analyses are conducted by using the symmetry-constrained elastic network model. As a result, the first three normal modes successfully capture the essence of transition pathway from wild type to CW-biased state. Their cumulative square overlap value reaches up to 0.842. Remarkably, it is also noted from the transition pathway that the cascade of interactions from the signal protein to FliM to FliG, highlighted by the major mode shapes from the first three normal modes, induces the reorientation (∼100° rotation of FliGC5) of FliG C-terminal that directly interacts with the stator protein. Presumably, the rotational direction of the motor protein is switched by this substantial change in the stator-rotor interaction.

Normal mode-guided transition pathway generation in proteins.

The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this.

Holographic deep learning for rapid optical screening of anthrax spores.

Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.

Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning.

Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.

A late-lineage murine neutrophil precursor population exhibits dynamic changes during demand-adapted granulopoiesis.

Homeostasis of neutrophils-the blood cells that respond first to infection and tissue injury-is critical for the regulation of immune responses and regulated through granulopoiesis, a multi-stage process by which neutrophils differentiate from hematopoietic stem cells. Granulopoiesis is a highly dynamic process and altered in certain clinical conditions, such as pathologic and iatrogenic neutropenia, described as demand-adapted granulopoiesis. The regulation of granulopoiesis under stress is not completely understood because studies of granulopoiesis dynamics have been hampered by technical limitations in defining neutrophil precursors. Here, we define a population of neutrophil precursor cells in the bone marrow with unprecedented purity, characterized by the lineage-CD11b+Ly6GloLy6BintCD115-, which we call NeuPs (Neutrophil Precursors). We demonstrated that NeuPs differentiate into mature and functional neutrophils both in vitro and in vivo. By analyzing the gene expression profiles of NeuPs, we also identified NeuP stage-specific genes and characterized patterns of gene regulation throughout granulopoiesis. Importantly, we found that NeuPs have the potential to proliferate, but the proliferation decreased in multiple different hematopoietic stress settings, indicating that proliferating NeuPs are poised at a critical step to regulate granulopoiesis. Our findings will facilitate understanding how the hematopoietic system maintains homeostasis and copes with the demands of granulopoiesis.