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.

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.

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.

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.

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.

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.

Robust elastic network model: A general modeling for precise understanding of protein dynamics.

In the study of protein dynamics relevant to functions, normal mode analysis based on elastic network models (ENMs) has become popular. These models are usually validated by comparing the calculated atomic fluctuation for a single protein in a vacuum to experimental temperature factors in the crystal packing state. Without reflecting the crystal packing effect, in addition, their arbitrary assignment of spring constants leads to inaccurate simulation results, yielding a low correlation of the B-factor. To overcome this limitation, we propose a robust elastic network model (RENM) that not only considers the crystalline effect by using symmetric constraint information but also uses lumped masses and specific spring constants based on the type of amino acids and chemical interactions, respectively. Simulation results with more than 500 protein structures verify qualitatively and quantitatively that one can obtain the better correlation of the B-factor by RENM without additional computational burden. Moreover, an optimal spring constant in physical units (dyne/cm) is quantitatively determined as a function of the temperature at 100 and 290K, which enables us to predict the atomic fluctuations and vibrational density of states (VDOS) without a fitting process. The additional investigation of 80 high-resolution crystal structures with anisotropic displacement parameters (ADPs) indicates that RENM could give a full description of vibrational characteristics of individual residues in proteins.

Label-free identification of individual bacteria using Fourier transform light scattering.

Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.

Ternary and senary representations using DNA double-crossover tiles.

The information capacity of DNA double-crossover (DX) tiles was successfully increased beyond a binary representation to higher base representations. By controlling the length and the position of DNA hairpins on the DX tile, ternary and senary (base-3 and base-6) digit representations were realized and verified by atomic force microscopy. Also, normal mode analysis was carried out to study the mechanical characteristics of each structure.

Vibrational characteristics of graphene sheets elucidated using an elastic network model.

Recent studies of graphene have demonstrated its great potential for highly sensitive resonators. In order to capture the intrinsic vibrational characteristics of graphene, we propose an atomistic modeling method called the elastic network model (ENM), in which a graphene sheet is modeled as a mass-spring network of adjacent atoms connected by various linear springs with specific bond ratios. Normal mode analysis (NMA) reveals the various vibrational features of bi-layer graphene sheets (BLGSs) clamped at two edges. We also propose a coarse-graining (CG) method to extend our graphene study into the meso- and macroscales, at which experimental measurements and synthesis of graphene become practical. The simulation results show good agreement with experimental observations. Therefore, the proposed ENM approach will not only shed light on the theoretical study of graphene mechanics, but also play an important role in the design of highly-sensitive graphene-based resonators.

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.

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.

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.

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.

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.

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.

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.