Structural Determinants of Chirally Selective Transport of Amino Acids through the α-Hemolysin Protein Nanopores of Free-Standing Planar Lipid Membranes.

Despite the importance of the enantioselective transport of amino acids through transmembrane protein nanopores from fundamental and practical perspectives, little has been explored to date. Here, we study the transport of amino acids through α-hemolysin (αHL) protein pores incorporated into a free-standing lipid membrane. By measuring the transport of 13 different amino acids through the αHL pores, we discover that the molecular size of the amino acids and their capability to form hydrogen bonds with the pore surface determine the chiral selectivity. Molecular dynamics simulations corroborate our findings by revealing the enantioselective molecular-level interactions between the amino acid enantiomers and the αHL pore. Our work is the first to present the determinants for chiral selectivity using αHL protein as a molecular filter.

Nanostructured Molecular Packing of Polymer Films Formed on Water Surfaces.

In this study, we examined the nanostructured molecular packing and orientations of poly[[N,N'-bis(2-octyldodecyl)-naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5'-(2,2'-bithiophene)] (P(NDI2OD-T2)) films formed on water for the application of nanotechnology-based organic electronic devices. First, the nanoscale molecule-substrate interaction between the polymer and water was modulated by controlling the alkyl side chain length in NDI-based copolymers. Increasing alkyl side chain lengths induced a nanomorphological transition from face-on to edge-on orientation, confirmed by molecular dynamics simulations revealing nanostructural behavior. Second, the nanoscale intermolecular interactions of P(NDI2OD-T2) were controlled by varying the volume ratio of the high-boiling-point additive solvent in the binary solvent blends. As the additive solvent ratio increased, the nanostructured molecular orientation of the P(NDI2OD-T2) films on water changed remarkably from edge-on to bimodal with more face-on crystallites, thereby affecting charge transport. Our finding provides essential insights for precise nanoscale morphological control on water substrates, enabling the formation of high-performance polymer films for organic electronic devices.

The mechanics and dynamics of cancer cells sensing noisy 3D contact guidance.

Contact guidance is a major physical cue that modulates cancer cell morphology and motility, and is directly linked to the prognosis of cancer patients. Under physiological conditions, particularly in the three-dimensional (3D) extracellular matrix (ECM), the disordered assembly of fibers presents a complex directional bias to the cells. It is unclear how cancer cells respond to these noncoherent contact guidance cues. Here we combine quantitative experiments, theoretical analysis, and computational modeling to study the morphological and migrational responses of breast cancer cells to 3D collagen ECM with varying degrees of fiber alignment. We quantify the strength of contact guidance using directional coherence of ECM fibers, and find that stronger contact guidance causes cells to polarize more strongly along the principal direction of the fibers. Interestingly, sensitivity to contact guidance is positively correlated with cell aspect ratio, with elongated cells responding more strongly to ECM alignment than rounded cells. Both experiments and simulations show that cell-ECM adhesions and actomyosin contractility modulate cell responses to contact guidance by inducing a population shift between rounded and elongated cells. We also find that cells rapidly change their morphology when navigating the ECM, and that ECM fiber coherence modulates cell transition rates between different morphological phenotypes. Taken together, we find that subcellular processes that integrate conflicting mechanical cues determine cell morphology, which predicts the polarization and migration dynamics of cancer cells in 3D ECM.

Multi-System-Level Analysis Reveals Differential Expression of Stress Response-Associated Genes in Inflammatory Solar Lentigo.

Although the pathogenesis of solar lentigo (SL) involves chronic ultraviolet (UV) exposure, cellular senescence, and upregulated melanogenesis, underlying molecular-level mechanisms associated with SL remain unclear. The aim of this study was to investigate the gene regulatory mechanisms intimately linked to inflammation in SL. Skin samples from patients with SL with or without histological inflammatory features were obtained. RNA-seq data from the samples were analyzed via multiple analysis approaches, including exploration of core inflammatory gene alterations, identifying functional pathways at both transcription and protein levels, comparison of inflammatory module (gene clusters) activation levels, and analyzing correlations between modules. These analyses disclosed specific core genes implicated in oxidative stress, especially the upregulation of nuclear factor kappa B in the inflammatory SLs, while genes associated with protective mechanisms, such as SLC6A9, were highly expressed in the non-inflammatory SLs. For inflammatory modules, Extracellular Immunity and Mitochondrial Innate Immunity were exclusively upregulated in the inflammatory SL. Analysis of protein-protein interactions revealed the significance of CXCR3 upregulation in the pathogenesis of inflammatory SL. In conclusion, the upregulation of stress response-associated genes and inflammatory pathways in response to UV-induced oxidative stress implies their involvement in the pathogenesis of inflammatory SL.

Super Proton Conductivity Through Control of Hydrogen-Bonding Networks in Flexible Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) have received much attention as a solid-state electrolyte in proton exchange membrane fuel cells. The introduction of proton carriers and functional groups into MOFs can improve the proton conductivity attributed to the formation of hydrogen-bonding networks, while the underlying synergistic mechanism is still unclear. Here, a series of flexible MOFs (MIL-88B, [Fe3 O(OH)(H2 O)2 (O2 C-C6 H4 -CO2 )3 ] with imidazole) is designed to modify the hydrogen-bonding networks and investigate the resulting proton-conducting characteristics by controlling the breathing behaviors. The breathing behavior is tuned by varying the amount of adsorbed imidazole into pore (small breathing (SB) and large breathing (LB)) and introducing functional groups onto ligands (-NH2 , -SO3 H), resulting in four kinds of imidazole-loaded MOFs-Im@MIL-88B-SB, Im@MIL-88B-LB, Im@MIL-88B-NH2 , and Im@MIL-88B-SO3 H. Im@MIL-88B-LB without functional groups exhibits the highest proton conductivity of 8.93 × 10-2  S cm-1 at 60 °C and 95% relative humidity among imidazole-loaded proton conductors despite the mild condition, indicating that functional groups may not be always required to enhance proton conductivity. The elaborately controlled pore size and host-guest interaction in flexible MOFs through imidazole-dependent structural transformation are translated into the high proton concentration without the limitation of proton mobility, contributing to the formation of effective hydrogen-bonding networks in imidazole conducting media.

Design of New Inorganic Crystals with the Desired Composition Using Deep Learning.

New solid-state materials have been discovered using various approaches from atom substitution in density functional theory (DFT) to generative models in machine learning. Recently, generative models have shown promising performance in finding new materials. Crystal generation with deep learning has been applied in various methods to discover new crystals. However, most generative models can only be applied to materials with specific elements or generate structures with random compositions. In this work, we developed a model that can generate crystals with desired compositions based on a crystal diffusion variational autoencoder. We generated crystal structures for 14 compositions of three types of materials in different applications. The generated structures were further stabilized using DFT calculations. We found the most stable structures in the existing database for all but one composition, even though eight compositions among them were not in the data set trained in a crystal diffusion variational autoencoder. This substantiates the prospect of the generation of an extensive range of compositions. Finally, 205 unique new crystal materials with energy above hull <100 meV/atom were generated. Moreover, we compared the average formation energy of the crystals generated from five compositions, two of which were hypothetical, with that of traditional methods like atom substitution and a generative model. The generated structures had lower formation energy than those of other models, except for one composition. These results demonstrate that our approach can be applied stably in various fields to design stable inorganic materials based on machine learning.

Tethering Effects in Oligomer-Based Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) can be constructed using conventional molecular linkers or polymeric linkers (polyMOFs), but the relationship and relative properties of these related materials remain understudied. As an intermediate between these two extremes, a library of oligomeric ligand precursors (dimers, trimers) was used to prepare a series of oligomeric-linker MOFs (oligoMOFs) based on the prototypical IRMOF-1 system. IRMOF-1 was found to be remarkably tolerant to a wide variety of oligomeric linkers, the use of which greatly enhanced the MOF yield and prevented framework interpenetration. Tether length-dependent ordering of ligand and metal cluster orientations was also observed in these oligoMOFs. Improved low-humidity stability was found in oligoIRMOF-1 samples, with surface area preservation varying as a function of tether length and a complete suppression of crystalline hydrolysis products for all oligoIRMOF-1 materials. These findings pave the way toward a better understanding of the structure-function relationships between monomeric, oligomeric, and polymeric MOFs and highlight an underutilized strategy for tuning MOF properties.

Understanding the Structural Collapse during Activation of Metal-Organic Frameworks with Copper Paddlewheels.

Many metal-organic frameworks (MOFs) suffer from stability issues as they can be easily amorphized from various external stimuli. In particular, it is common to observe structural collapse during the activation process of removing the synthesis solvent. In this study, we conduct high-throughput computational analysis that focuses on the activation status of MOFs that possess copper paddlewheel metal nodes. From the analysis, various mechanical properties (e.g., bulk, Young's, and shear moduli) were found to be good predictors for collapse. Furthermore, we have identified anomaly MOFs with good mechanical stability that were previously reported to collapse. Accordingly, the activation process was reattempted with improved techniques, and one of these MOFs was successfully activated.

Mining Insights on Metal-Organic Framework Synthesis from Scientific Literature Texts.

Identifying optimal synthesis conditions for metal-organic frameworks (MOFs) is a major challenge that can serve as a bottleneck for new materials discovery and development. A trial-and-error approach that relies on a chemist's intuition and knowledge has limitations in efficiency due to the large MOF synthesis space. To this end, 46,701 MOFs were data mined using our in-house developed code to extract their synthesis information from 28,565 MOF papers. The joint machine-learning/rule-based algorithm yields an average F1 score of 90.3% across different synthesis parameters (i.e., metal precursors, organic precursors, solvents, temperature, time, and composition). From this data set, a positive-unlabeled learning algorithm was developed to predict the synthesis of a given MOF material using synthesis conditions as inputs, and this algorithm successfully predicted successful synthesis in 83.1% of the synthesized data in the test set. Finally, our model correctly predicted three amorphous MOFs (with their representative experimental synthesis conditions) as having low synthesizability scores, while the counterpart crystalline MOFs showed high synthesizability scores. Our results show that big data extracted from the texts of MOF papers can be used to rationally predict synthesis conditions for these materials, which can accelerate the speed in which new MOFs are synthesized.

Structural basis of the fanconi anemia-associated mutations within the FANCA and FANCG complex.

Monoubiquitination of the Fanconi anemia complementation group D2 (FANCD2) protein by the FA core ubiquitin ligase complex is the central event in the FA pathway. FANCA and FANCG play major roles in the nuclear localization of the FA core complex. Mutations of these two genes are the most frequently observed genetic alterations in FA patients, and most point mutations in FANCA are clustered in the C-terminal domain (CTD). To understand the basis of the FA-associated FANCA mutations, we determined the cryo-electron microscopy (EM) structures of Xenopus laevis FANCA alone at 3.35 Å and 3.46 Å resolution and two distinct FANCA-FANCG complexes at 4.59 and 4.84 Å resolution, respectively. The FANCA CTD adopts an arc-shaped solenoid structure that forms a pseudo-symmetric dimer through its outer surface. FA- and cancer-associated point mutations are widely distributed over the CTD. The two different complex structures capture independent interactions of FANCG with either FANCA C-terminal HEAT repeats, or the N-terminal region. We show that mutations that disturb either of these two interactions prevent the nuclear localization of FANCA, thereby leading to an FA pathway defect. The structure provides insights into the function of FANCA CTD, and provides a framework for understanding FA- and cancer-associated mutations.

Association between body mass index and fragility fracture in postmenopausal women: a cross-sectional study using Korean National Health and Nutrition Examination Survey 2008-2009 (KNHANES IV).

BACKGROUND: The present study examined the relationship between body mass index (BMI) and the risk for fragility fractures in postmenopausal Korean women. METHODS: Among subjects who participated in the 4th Korea National Health and Nutrition Examination Survey (2008-2009), 2114 women ≥ 40 years of age were included. BMI was based on standards set by the Korean Society for the Study of Obesity, as follows: < 18.5 kg/m2, underweight; 18.5 ≤ to < 25 kg/m2, normal weight; and ≥ 25 kg/m2, obese. Subjects were also divided into three groups according to the location of fragility fracture: spine, hip, or wrist. RESULTS: The mean (± SD) rate of fragility fracture was significantly different among the three groups: 5.9 ± 2.9% (underweight), 1.1 ± 0.3% (normal weight), and 3.0 ± 0.7% (obese) (p = 0.001). After correcting for age, family history, and treatment history of osteoporosis and rheumatoid arthritis, smoking and drinking status, and level of exercise, multivariable regression analysis revealed that the odds ratio for fragility fracture in the underweight group was 5.48 [95% confidence interval (CI) 1.80-16.73] and 3.33 (95% CI 1.61-6.87) in the obese group. After subdividing fragility fractures into vertebral and non-vertebral, the odds ratio for vertebral fracture in the underweight group was 5.49 (95% CI 1.31-23.09) times higher than that in the normal weight group; in the obese group, the non-vertebral fracture odds ratio was 3.87 (95% CI 1.45-10.33) times higher. Analysis of non-vertebral fractures in the obese group revealed an odds ratio for fracture 22.05 (95% CI 1.33-365.31) times higher for hip fracture and 3.85 (95% CI 1.35-10.93) times higher for wrist fracture. CONCLUSIONS: Obesity and underweight increased the risk for fragility fractures in postmenopausal Korean women.

Void Space versus Surface Functionalization for Proton Conduction in Metal-Organic Frameworks.

Void space and functionality of the pore surface are important structural factors for proton-conductive metal-organic frameworks (MOFs) impregnated with conducting media. However, no clear study has compared their priority factors, which need to be considered when designing proton-conductive MOFs. Herein, we demonstrate the effects of void space and pore-surface modification on proton conduction in MOFs through the surface-modified isoreticular MOF-74(Ni) series [Ni2 (dobdc or dobpdc), dobdc=2,5-dihydroxy-1,4-benzenedicarboxylate and dobpdc=4,4'-dihydroxy-(1,1'-biphenyl)-3,3'-dicarboxylate]. The MOF with lower porosity with the same surface functionality showed higher proton conductivity than that with higher porosity despite including a smaller amount of conducting medium. Density functional theory calculations suggest that strong hydrogen bonding between molecules of the conducting medium at high porosity is inefficient in inducing high proton conductivity.

Geometric Dependence of 3D Collective Cancer Invasion.

Metastasis of mesenchymal tumor cells is traditionally considered as a single-cell process. Here, we report an emergent collective phenomenon in which the dissemination rate of mesenchymal breast cancer cells from three-dimensional tumors depends on the tumor geometry. Combining experimental measurements and computational modeling, we demonstrate that the collective dynamics is coordinated by the mechanical feedback between individual cells and their surrounding extracellular matrix (ECM). We find the tissue-like fibrous ECM supports long-range physical interactions between cells, which turn geometric cues into regulated cell dissemination dynamics. Our results suggest that migrating cells in three-dimensional ECM represent a distinct class of an active particle system in which the collective dynamics is governed by the remodeling of the environment rather than direct particle-particle interactions.

Finding Hidden Signals in Chemical Sensors Using Deep Learning.

Achieving high signal-to-noise ratio in chemical and biological sensors enables accurate detection of target analytes. Unfortunately, below the limit of detection (LOD), it becomes difficult to detect the presence of small amounts of analytes and extract useful information via any of the conventional methods. In this work, we examine the possibility of extracting "hidden signals" using deep neural network to enhance gas sensing below the LOD region. As a test case system, we conduct experiments for H2 sensing in six different metallic channels (Au, Cu, Mo, Ni, Pt, Pd) and demonstrate that deep neural network can enhance the sensing capabilities for H2 concentration below the LOD. We demonstrate that this technique could be universally used for different types of sensors and target analytes. Our approach can extract new information from the hidden signals, which can be crucial for next-generation chemical sensing applications and analytical chemistry.

Modeling adsorption properties of structurally deformed metal-organic frameworks using structure-property map.

Structural deformation and collapse in metal-organic frameworks (MOFs) can lead to loss of long-range order, making it a challenge to model these amorphous materials using conventional computational methods. In this work, we show that a structure-property map consisting of simulated data for crystalline MOFs can be used to indirectly obtain adsorption properties of structurally deformed MOFs. The structure-property map (with dimensions such as Henry coefficient, heat of adsorption, and pore volume) was constructed using a large data set of over 12000 crystalline MOFs from molecular simulations. By mapping the experimental data points of deformed SNU-200, MOF-5, and Ni-MOF-74 onto this structure-property map, we show that the experimentally deformed MOFs share similar adsorption properties with their nearest neighbor crystalline structures. Once the nearest neighbor crystalline MOFs for a deformed MOF are selected from a structure-property map at a specific condition, then the adsorption properties of these MOFs can be successfully transformed onto the degraded MOFs, leading to a new way to obtain properties of materials whose structural information is lost.

Comparative effects of dry-aging and wet-aging on physicochemical properties and digestibility of Hanwoo beef.

OBJECTIVE: The purpose of this study was to investigate the effects of aging methods (AM) i.e. dry-aging (DA) and wet-aging (WA) on the physicochemical properties and in vitro digestibility of proteins in beef short loin. METHODS: Short loins (M. longissmus lumborum), were trimmed and boned-out on the fifth day postmortem, from a total of 18 Hanwoo, which were purchased from a commercial slaughterhouse. Short loins were separated randomly grouped into one of the three treatments: control, WA (1°C, 7 days), and DA (1°C, 0.5 m/s, 85% relative humidity [RH], 30 days). RESULTS: Dry-aged beef (DAB) exhibited higher pH, water holding capacity (WHC), myofibrillar fragmentation index (MFI), and digestibility, however lower lightness, redness, and yellowness values, cooking loss, and shear force (SF), than those of wet-aged beef (WAB) (p<0.05). The myosin light chain band intensity of DAB was higher than that of control and WAB in sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The in vitro digestibility of aged beef was highly (p<0.001) correlated to physicochemical properties except WHC. The correlation coefficient between AMs and WHC was higher than that between AM and SF (p<0.05) or MFI (p<0.001). A high correlation was observed between SF and MFI (p<0.001). CONCLUSION: Thus, we believe that DAB is more advantageous than WAB owing to its high digestibility and WHC and low SF.

Novel inverse finite-element formulation for reconstruction of relative local stiffness in heterogeneous extra-cellular matrix and traction forces on active cells.

Accurately resolving the traction forces on active cells in 3D extra-cellular matrix (ECM) is crucial to understanding stress homeostasis in cellularized ECM systems and the resulting collective cellular behavior. The majority of 3D traction force microscopy techniques, which compute the stress distribution in ECM as well as cellular traction forces from experimentally measured deformation field in the ECM using dispersed tracing particles or fluorescently-tagged matrix proteins, have assumed a spatially homogeneous ECM with constant material properties at every location in the system. Recent studies have shown that ECM can exhibit significant heterogeneity due to the disordered nature of collagen network as well as cell remodeling. In this paper, we develop a novel inverse finite-element formulation for accurately resolving the cellular traction forces by explicitly reconstructing the relative local elastic modulus values of the heterogeneous ECM containing an arbitrary shaped cell from a measured displacement field in the ECM. Our formulation does not require any a priori knowledge of the boundary conditions, and simultaneously results in the distribution of the heterogeneous modulus values and stress field in the ECM, as well as the traction forces on the cell, given experimentally measured average modulus of the ECM. We first validate our procedure in artifical model cell-ECM systems, and then employ the procedure to compute the distribution of elastic modulus in a heterogeneous type-I collagen gel as well as the traction force on a rounded breast cancer cell in the gel, based on the deformation field data obtained via 3D reflectance force microscopy. Our results indicate that the majority part of the cell is in a tensile state, while a local region on the cell is in a tri-axial compressive state, indicating the possible development of a local protrusion in this region. This is further verified by tracking the subsequent evolution of the cell morphology.

Computational Analysis of Linker Defective Metal-Organic Frameworks for Membrane Separation Applications.

Intracrystalline defects in metal-organic frameworks (MOFs) are known to have crucial roles in dictating their material properties. In this work, computational simulations were used to induce linker vacancy defects in MOF membranes and to investigate their influence on H2/CH4 separation. Linker defective structures were created for the 228 candidate MOFs, and their separation performances were compared between the defective and the nondefective structures. Our results show that the existence of linker vacancy defects can lead to significant performance changes in the MOF membranes, and more importantly, the ranking of the best materials can differ with the defects present. This suggests the importance of taking into account the potential for defects when it comes to materials design for various membrane separation applications.

Physicochemical and sensory characteristics of commercial, frozen, dry, and wet-aged Hanwoo sirloins.

Objective: The objective of this study was to evaluate the physicochemical, sensory and taste characteristics of commercial, frozen, dry, and wet aged Hanwoo sirloin. Methods: Grade 2 sirloin from 6 Hanwoo steers (about 30 months old) were obtained after 5 days postmortem. Samples assigned into the following four groups commercial beef (CON), frozen beef (HF/40 days in -18°C freezer), wet-aged beef (HW/21 days), and dry-aged beef (HD/40 days) were stored in a 80±5% relative humidity cooler at 1 °C. Results: The HF group showed a significantly higher cooking loss and expressible drip with significantly higher pH compared to other groups. In addition, protein and fat contents in the HD group were higher than those in other groups (p < 0.05). The shear forces in the HW and HD groups were significantly lower than those in the CON group. The HD group had significantly higher omega-3 and polyunsaturated fatty acids compared with other groups. Glutamic acid levels in the HD group were significantly higher compared with those in other groups. Electronic tongue analysis revealed that sourness of the HD group was lower than that of other groups, whereas the HD group showed significantly higher umami, richness, and saltiness compared to other groups (p < 0.05). Sensory test results revealed that the HW group had significantly higher tenderness, while the HD group had significantly higher chewiness, juiciness, and overall acceptability scores. Conclusion: These results suggest that both wet- and dry-aging treatments can effectively improve sensory characteristics, and dry-aging was much more useful to enhance umami tastes and meat quality of 2 grade Hanwoo sirloins.

Effect of aged garlic powder on physicochemical characteristics, texture profiles, and oxidative stability of ready-to-eat pork patties.

OBJECTIVE: The aim of this study was to investigate the effects of aged garlic powder (AGP) on physicochemical characteristics, texture profiles, and oxidative stability of ready-to-eat (RTE) pork patties. METHODS: There were five treatment groups: a control; 1% fresh garlic powder (T1); 0.5%, 1%, and 2% AGP (T2, T3, and T4). Pork patties with vacuum packaging were roasted at 71°C for core temperature, stored at 4°C for 14 d, and then reheated for 1 min using a microwave. RESULTS: The AGP groups showed a lower the level of lipid oxidation and higher thiol contents than the control and T1. The pH value of the control increased whereas that of aged garlic groups decreased after re-heating process. In addition, the redness significantly increased with increasing level of AGP whereas the redness of the control and T1 decreased after re-heating process. T4 added patties improved textural and sensory properties compared to the control. CONCLUSION: The results of this study suggest that AGP addition to RTE pork patties can improve their sensory characteristics and oxidative stability.