The use of electrically conductive materials to impart electrical properties to substrates for cell attachment proliferation and differentiation represents an important strategy in the field of tissue engineering. This paper discusses the concept of electro-active structures and their roles in tissue engineering, accelerating cell proliferation and differentiation, consequently leading to tissue regeneration. The most relevant carbon-based materials used to produce electro-active structures are presented, and their main advantages and limitations are discussed in detail. Particular emphasis is put on the electrically conductive property, material synthesis and their applications on tissue engineering. Different technologies, allowing the fabrication of two-dimensional and three-dimensional structures in a controlled way, are also presented. Finally, challenges for future research are highlighted. This review shows that electrical stimulation plays an important role in modulating the growth of different types of cells. As highlighted, carbon nanomaterials, especially graphene and carbon nanotubes, have great potential for fabricating electro-active structures due to their exceptional electrical and surface properties, opening new routes for more efficient tissue engineering approaches. Tissue engineering is a relatively novel discipline, aiming at improving or replacing biological tissues. The use of scaffolds, physical substrates for cell attachment, proliferation and differentiation, is the most common strategy for tissue engineering ,2,3,4. TElectrical signals are critical physiological stimuli that strongly affect cell behavior due to the cell proliferation impact on the cell membrane potential ,6. ElectBassett et al. presented the first evidence of the electrical stimulation impact on tissues, by investigating the effects of electric current on bone regeneration in adult dogs . Tissue t represents the critical exponent related to the dimensionality of the conductive networks in the composite material. Composites with high −7–10−2 S/cm, depending on tissue [Polymeric tissue engineering scaffolds can be fabricated with or without the incorporation of fillers, aiming to enhance mechanical or biological. Several researchers investigated the incorporation of conductive carbon nanomaterials  into different polymer matrices to produce tissue engineering scaffolds ,28,29,30 process ,35. The n tissue .Different processing techniques have been explored to produce scaffolds with different dimensionalities and architectures. The so-called conventional fabrication methods produce scaffolds by using fiber bonding, gas foaming, high-pressure processing, hydrocarbon templating, liquid–liquid phase separation, melt moulding, membrane lamination, polymer or ceramic fiber composite foam, solvent casting and particulate leaching methods . These mElectrospinning and additive manufacturing are other relevant fabrication techniques. Electrospinning has been widely used to fabricate electro-active structures, in which process polymer and conducting materials are dissolved in a suitable solvent, and the polymer solution is dropped via a needle ,2. This This paper overviews the current state-of-the-art of using carbon nanomaterials  for the fabrication of electro-active structures  for tissue engineering applications. Electrical properties and synthesis methods of these carbon nanomaterials are presented. A wide range of fabrication techniques are considered, and several tissue engineering applications are discussed in detail. Future perspectives and developments are also presented.2 and sp3) with other carbon atoms and non-metallic elements [2 carbon materials are intrinsically anisotropic as it contains delocalized last non-hybridized valence π-electrons in a plane perpendicular to its basal plane. The mobility within the lattice and the dynamics in one particular configuration create “electronic layers”, responsible for the high 2D electric conductance [Carbon nanomaterials (CNMs) exhibit vast structural diversity, owing to carbon atom’s capability of covalently bonding at diverse hybridization states  hybridized having covalent σ bonds with three nearest carbon atoms, forming a robust honeycomb lattice. This makes graphene currently the strongest known material with Young’s modulus of ~1.0 TPa [Discovered in 2004, single-layer graphene is an atomically thin film of carbon atoms bonded together in a planar 2D structure. As illustrated in ~1.0 TPa . Moreove~1.0 TPa .The ambipolar field effect on few-layers graphene, which corresponds to the availability of carriers to be tuned continuously between holes and electrons by supplying the required gate bias, was first observed by Novoselov et al. . For posBesides the ambipolar field effect, graphene also shows the quantum Hall effect (QHE) and an extremely high carrier mobility ,62,63,642 V−1s−1 has been attained for suspended graphene and ~500,000 cm2 V−1s−1 for graphene-based field-effect transistor [This extraordinary electronic property is caused by the high quality of its 2D crystal lattice. In other words, graphene with higher defects density will have lower carrier mobility, since these defects act as the scattering centers, which inhibit charge transport . Perpendansistor ,64,67. Cansistor , making ansistor .Graphene can be produced by using top-down and bottom-up synthesis methods. Top-down synthesis methods of graphene are generally detachment or exfoliation from existing graphite crystals . ExfoliaMechanical exfoliation (repeated peeling), the first reported approach for graphene fabrication, was initially described by Novoselov et al. . MoreoveN-methylpyrrolidinone (NMP) [γsl), maximizing solid–liquid interfacial work of adhesion (Wsl) at the optical mw. Moreover the water–NMP (liquid–liquid) heteroassociation prevents the recombination of exfoliated layers, and the bulky (NMP·2H2O)n aggregates are able to provide intersheet repulsive forces, separating the nanosheets with non-overlapping Leonard–Jones (L–J) potentials. Briefly, 50 mg of bulk materials were placed in 14 mL centrifuge tubes with an initial concentration of 5 mg/mL for exfoliation. The materials were batch sonicated for 6 h at the power of 100 W and a frequency of 37 kHz. Every 30 min, the positions of each sample tubes were changed to achieve uniform power distribution and the water of bath sonicator was replaced to maintain the temperature between 27 and 37 °C during the sonication process. The dispersions were stored overnight and centrifuged at 3000 rpm for 30 min. According to TEM measurement, the lateral size of the exfoliated graphene was 500–2000 nm, the optimal water–NMP mixed solvent mass fraction was 0.2–0.3, which result to 0.43 (~8.6% by mass) mg/mL of exfoliated graphene nanosheets [Liquid-phase exfoliation (LPE) is another synthesis method being characterized by its low cost, ease of operation and minimal environmental impact. Manna et al. demonstrated single- and few-layers of graphene nanosheets synthesis from bulk materials by a surfactant-free LPE, using water as the co-solvent with ne (NMP) . Authorsne (NMP) . Layerednosheets . Howevernosheets .2H4 and NaBH4 [2 expansion during rapid heating of GOs. Therefore, complete graphite oxidation and extremely rapid heating of GOs are fundamentally required. Briefly, natural flake graphite was oxidized in a mixture solution of sulfuric acid, nitric acid and KClO3 for more than 96 h. After the 0.34 nm intergraphene spacing disappears, and a new spacing of 0.65–0.75 nm range appears (depend on GOs water content), the GOs are dried and purged with argon for thermal exfoliation. The rapid heating rate of 2000 °C/min to 1050 °C would split the GOs into several individual sheets through CO2 evolution. Successful exfoliation was confirmed when all diffraction peaks were eliminated. Atomic force microscopy (AFM) measurements show that the produced graphene sheets are well dispersed at an average density of about 50 flakes per 100 μm2 and exhibit a lateral extent of a few hundred nanometers. The representative height varies at two length scales, 2 nm for the flat areas with respect to HOPG and 10 nm for the several large, meandering wrinkles [Oxidation-reduction (redox) is another top-down synthesis method. GOs produced by Hummers method can be reduced into graphene with different kinds of reducing agents, such as Nnd NaBH4 ,81. Nevend NaBH4 . Schniepnd NaBH4 , based owrinkles .On the other hand, bottom-up synthesis deal with directly growing graphene layers on substrate surfaces. This method includes epitaxial growth on silicon carbide crystal and chemical vapour deposition (CVD) where graphene from a hydrocarbon source precipitates from the transition metal surface ,85. SyntDue to the ease of controllability and scalability, graphene films with large area and high quality can be obtained via CVD process . Figure Adsorption and catalytic decomposition of precursor gas.Diffusion and dissolution of decomposed carbon species on the surface and into the bulk metal.Segregation of dissolved carbon atoms onto the metal surface.Surface nucleation and growth of graphene.The graphene growth on the metal substrate based on heterogeneous catalysis CVD process consists of four steps:Another different route occurs for metal with poor carbon affinity , in which the decomposition of carbon precursors was directly followed by graphene formation, realized by diffusion of carbon atoms on the metal surface. These two routes coexist in all graphene CVD system, but dominant depends on the properties of metal substrates .2/Si substrates. The samples were heated to 1000 °C in a quartz tube under an argon atmosphere. After flowing the reaction gas mixtures (CH4:H2:Ar = 50:65:200 standard cubic centimeters per minute (sccm) ), the samples were rapidly cooled to room temperature (~10 °C/s), using flowing argon, which is essential to prevent the multi-layers formation and efficiently separate graphene layers in the later process [Somani et al. reported that few-layered graphene could be obtained by CVD synthesis on nickel sheets . Similar process . Li et a process .Different synthesis methods significantly affect the properties of graphene such as surface area, number of layers, lateral dimension, surface chemistry, hydrophilicity and purity. These parameters also have an impact on the biological effects of graphene . It is rAfter production, graphene can be reformed into zero-dimensional nanomaterial, rolled into one-dimensional nanotube or manipulated into 3D graphite . DispersTang et al. examined the development of neural network from human neural stem cells (hNSCs) differentiation at graphene by comparing fluorescence images from day 1 to day 14 . After sβ-tubulin (Tuj1) and microtubule-associated protein 2 (MAP2) significantly increased after 14 days of cell differentiation [Jakus et al. prepared a custom-sized nerve conduit based on graphene and poly (lactide-co-glycolide) (PLG), using an extrusion-based additive manufacturing technology . Resultsntiation .Zhang et al. developed an approach which successfully added graphene into regenerated silk fibroin (RSF) scaffolds. As ε-caprolactone) (PCL)/graphene scaffolds. The effect of adding graphene to the polymeric scaffolds was studied form a morphological, physiochemical and biological point , using a thermal-initiated free-radical polymerization method. In vitro biological assessments suggested that the seeded chondrocytes were able to attach proliferate. Moreover, for the in vivo biological assessment on osteochondral defects of a rabbit model, compared to the scaffold without cells, scaffolds with cell injection induced higher volume of newly formed cartilage/bone tissues [For cartilage tissue engineering applications, Liao et al.  fabricat tissues .2+ handling properties of mouse embryonic stem cell derived cardiomyocytes (mES-CM). This can be explained by the local conductive pathways of the scaffolds which facilitated signal propagation and interaction between cells [−1, thus stimulating a high level of the cardiac-specific genes Conx43 and TrpT-2 after seven days of cell seeding without the use of external electrical stimulation [Hitscherich et al. investigated the potential of PCL/graphene scaffold for cardiac tissue engineering applications. The scaffolds were prepared through electrospinning considering different graphene concentrations (0.01% and 0.5%). Electrical stimulation results show that the impedance of the scaffolds decreased by increasing the graphene contents. In vitro studies indicate that the fabricated scaffolds were biocompatible, able to support stem cell-derived cardiomyocytes, and to improve the Caen cells . Bahramimulation .Additional biological studies using graphene electro-active structures are summarized in 2 bonded carbon atoms, conceptually regarded as rolled-up sheets of graphene [2 carbon and tunable physical properties.Carbon nanotubes (CNTs) are cylindrical tubes of spgraphene . CNTs argraphene . CNTs cagraphene ,143. SWCgraphene , while Mgraphene . AccordiContrary to graphene, QHE does not exist in CNTs. The properties of CNTs can be tuned by controlling the diameter, length, single-walled or multi-walled, surface functionalization and chirality ,150. Dopn,m), which are equivalent to the diameter (d) and the chiral angle (α) [m index is assigned to mth hexagon from the origin, whereas the n index is ascribed as α. Therefore, CNTs structure can be uniquely determined when the diameter and the angle are known [The structure of SWCNTs can be represented by the chirality indices (ngle (α) ,154. Figre known . Armchaire known . MWCNTs re known .Three different methods can be used to synthesize CNTs (MWCNTs and SWCNTs) b–d: CVD The key factor in controlling the chirality of SWCNTs in these CVD methods is the initial nucleation stage, in which the hemispherical cap composed of six pentagons are formed , and the2H2 and 50 sccm H2 and heated at 600 °C. The chiral indices of the produced SWCNTs with different nanocatalysts compositions were analyzed by photoluminescence (PL) characterization  distribution was obtained for SWCNTs grown with Ni0.27Fe0.73 nanocatalysts with dominating structure of  and smaller fractions of , ,  and  [Many metal catalysts such as bimetallic catalysts have been used for catalytic chiral specific CNTs growth ,174. Howrization b–e. The d  .n, m), using W6Co7 alloy nanocatalysts. Briefly, SWCNT growth was performed in a quartz reactor with an inner diameter of 2.1 cm by using an ethanol CVD method. The precursor solution was dropped onto the SiO2/Si substrates and was calcined at 700 °C in an air containing tube furnace for 3 min. After purging the system with Ar, two hydrogen flows (80 cm3/min and then 200 cm3/min) were introduced through a water bubbler (ice-water bath) to reduce the calcined catalyst precursors, using a TPR method from 800 to 1050 °C, for 4 min. Then 200 cm3/min of H2 flow was introduced into the system to remove the water vapour prior to the nanotube growth. Finally, CNT can be grown by flowing 200–300 cm3/min of Ar through an ethanol bubbler and 20–50 cm3/min of H2 for 10 min, followed by cooling under atmospheric H2 and Ar, respectively [The key point in achieving nanotube with specific chiral indices is to maximize the structural match between the nanocatalysts crystal planes and the end structure of nanotube chirality ,169,170.6Co7 alloy nanocatalysts were reduced from W39Co6Ox under different temperature and gaseous phase environments to tune the intermetallic W6Co7 nanocrystals. Under optimized CVD conditions, the (0 0 12), (1 1 6) and (1 0 10) crystal plane of W6Co7 individually favors the growth of , zigzag  and  SWCNTs, respectively [The Wectively ,169,170.ectively .The synthesis process of CNTs will affect the biological properties as a consequent of different lengths and diameters (aspect ratios) of the produced CNT. It is reported that a high aspect ratio has a detrimental effect on cells . This isReports show that carbon nanotubes are excellent functioning fillers for electro-active scaffolds relevant to a wide range of tissue engineering applications. When in contact with CNTs, cells have been found to become more electrically active, mature and better interconnected. The high aspect ratio of CNTs can structurally simulate certain elongated biomolecules  useful to mimic the morphology of heart and nerve tissues ,183,184.w/v)), while Shin et al. [For heart tissue engineering applications, Shin et al. and Ahadian et al. ,185 inven et al.  producedw/w%) on both mechanical and biological properties of the scaffolds. Nano-indentation studies showed a gradual enhancement in the elastic modulus (increased from 0.51 ± 0.18 GPa for PCL scaffold to 0.87 ± 0.10 GPa for PCL scaffolds containing 5% of MWCNT), hardness (increased from 0.057 ± 0.010 GPa to 0.072 ± 0.003 GPa) and maximum peak load (increased from 1.16 to 1.34 mN). The addition of MWCNTs also contributes to increase the crystallinity level of printed filament, broadening the crystallization peak, due to the restricted mobility of polymer chains in the nanocomposite matrix. MTT assay with H9C2 rat myocardial cells showed no cytotoxic due to the presence of MWCNTs [w/v%), using an electrohydrodynamic 3D printing system. The PCL solution was prepared by using acetic acid, and PEO was added to modulate the viscosity. Biological results show that the addition of MWCNT facilitate cell alignment but had a negative effect on cell attachment, due to its agglomeration in the printed fibers, compared to PCL–PEO scaffolds.Ho et al. fabricated 3D porous PCL/MWCNT scaffolds with filament distance ranging from 300 to 450 µm with an extrusion-based additive manufacturing system . Authorsf MWCNTs . He et af MWCNTs . In thisKharaziha et al.  reportedw/v%, and the concentration of CNTs was 0.2 wt.%. Results show that the incorporation of the ionically modified CNTs improved the compressive strength (from 1.5 MPa for PCL scaffolds and 2.0 MPa for PCL/HA scaffolds to 5.5 MPa for PCL/HA/CNT scaffolds) and MC3T3-E1 cell attachment and proliferation. In vivo tests were conducted by implanting the PCL/HA/CNT scaffold into a rat subcutaneous tissue. After four weeks, the results show signs of inflammatory effects due to the presence of the scaffold and the formation of soft fibrous tissue and neo-blood vessels [For bone tissue engineering applications, Goncalves et al. produced three-phase interconnected porous scaffolds (hydroxyapatite and CNTs mixed with PCL) with different compositions  and pore size ranging between 450 and 700 µm, using a pressure-assisted additive manufacturing system . Biologi vessels .Wang et al.  investigFor nerve tissue engineering applications, electro-active scaffolds containing CNTs have been produced by using different polymers and hydrogels. Sang et al. produced single-walled carbon nanotube–poly(n-isopropylacrylamide) (SWCNT–PNIPAAm) structures through copolymerization of n-isopropylacrylamide and single-walled carbon nanotubes . The effZPEDOT/CNT| = 6 kΩ) scaffolds at 0.1 Hz was significantly lower than PDMS/CNT (|ZPDMS/CNT| = 50 kΩ) and naked electrode filled with electrolyte PBS solution (|ZPBS| = 90 kΩ). The fabricated scaffolds also show good biocompatibility with mouse astrocytes C8-D1A cells and have a positive effect on promoting cell growth in the first three days [For other applications, Dominguez-Alfaro et al.  fabricatree days . Jayaramree days .Other techniques like freeze-casting and electrospinning were also used to produce different types of polymer/CNT polymer/ceramic/CNT and bioglass/CNT scaffolds ,202,203.Despite the successful use of CNTs as reinforcements of a wide range of polymer based scaffolds and substrates, the biocompatibility and biosafety of CNTs still require further investigation . It has The field of tissue engineering is experienced exciting advances toward the fabrication of smart and biomimetic constructs as alternatives to current clinical therapies. These advances strongly rely on the use of advanced materials and new fabrication techniques. Electrospinning and additive manufacturing have been successfully explored to produce scaffolds for skin, bone, nerve and muscle regeneration. Usually, these techniques use single material  to produce cell substrates designed according to specific requirements like porosity, mechanical properties, surface properties and degradation characteristics. Recent studies showed the relevance of using materials to stimulate cells increasing cell attachment, proliferation and differentiation. Electrically conductive materials, as discussed in this paper, could have a significant impact in tissue engineering, as there is an evidence that electrical stimulation is useful for stimulate-guided growth of cells.Scaffolds made with different polymers reinforced with a wide range of electrically conductive materials have been proposed and assessed from a biological point of view. As discussed in this review, the exceptional electrical and surface properties of CNMs, especially graphene and CNTs, together with their controllable morphologies make them important components for the development of novel electro-active scaffolds. A wide range of 2D membranes were initially produced and assessed with different cell lines. Recently, the development of additive manufacturing enabled the use of CNMs blended with polymers to create 3D porous scaffolds in a controlled and reproducible way. Through additive manufacturing, it will also be possible to create complex multi-material and functional gradient scaffolds, containing different regions with and without electrically conductive fillers. Functional gradient scaffolds like these can be relevant to produce tissue interfaces, using hADSCs, where the elastic and flexible nature of CNMs not only improves the mechanical properties of the scaffolds, but also synergize the effects of electrical stimulation on both cell proliferation and differentiation. However, very few papers compare the performance of different CNMs. Wang et al. reported that graphene filler presents better chemical and physical properties than CNT under the same amount , while SDifferent strategies to incorporate CNMs have been explored. The majority of the studies, as reported in this review, used chemical or physical blending approaches. However, in this case, techniques such as additive manufacturing or electrospinning cannot allow the fabrication of structures with high levels of CNMs due to rheological constrains. However, some researchers also explored new routes to create core–shell structures with CNMs as the outer layer and a polymeric material as the core . This apThis review mainly focuses on the biological performance of electro-active structures. However, several research works also demonstrated the antimicrobial properties of polymer/CNM scaffolds. CNMs such as graphene have been identified to have antibactericidal activity on various bacteria due to their sharp edges and oxidative stress induction. This should be further explored, for example to create new tissue engineering scaffolds with improved biological and antibacterial properties. Currently, the design of scaffolds for tissue engineering applications neglects the important issue of bacterial infection of the damaged tissue.As presented in this review, electro-active scaffolds containing small amounts of CNMs were successfully used for tissue regeneration. However, a possible alternative route, not fully explored in this paper, is the fabrication of structures containing a very high concentration of CNMs, not to stimulate cell proliferation and differentiation, but cell apoptosis, targeting cancer treatment application.Current major challenges are related to the lack of comprehensive in vivo studies for adequate assessment of long-term effects in terms of biocompatibility and cytotoxicity. The critical cytotoxicity concentration of these materials for different cell types is not yet clear. Their cellular and biological interactions, especially relating to cellular uptake mechanisms and biocorona formation, is not fully understood either. These require further investigations, conducting systematic comparative studies which will differentiate results of humans from animals.Challenges regarding the synthesis of CNMs with controlled size, shapes and functionalities should also be addressed as it affects the overall biocompatibility and performance of CNM-based electro-active structures. In the case of electro-active porous scaffolds, a key parameter is the control of its degradability, which must match the regeneration rate of the new tissue. However, the in vivo biodegradability of the electrically conductive carbon nanomaterials is still not fully investigated, representing an important research challenge. In-depth in vivo experiments for better understanding the interaction between electro-active structures and the surrounding tissues and their performance toward tissue regeneration are still required.


The load capacity of reinforced concrete structure will decrease by chloride ingress in coastal region. In this paper, the corrosion probability and flexural strength of a typical reinforced concrete beam design under the influence of temperature and humidity was obtained by the Monte Carlo method. The relationship between flexural strength, temperature, relative humidity, and geometric parameters was established. The annual average temperature and relative humidity were treated as random variables together with the geometric size and concrete compressive strength. The corrosion probability and flexural strength in a wave splashing zone, coastal atmospheric zone, and offshore atmospheric zone were calculated. The results show that the corrosion probabilities of the three regions are obviously different. When the standard deviation of temperature is less than 1.5 °C, the temperature can be treated as a constant in the calculation of the concrete cracking probability in the wave splashing zone. A binary logistic regression formula was given to predict whether the randomness of temperature and humidity should be considered in the offshore atmospheric zone. When the standard deviation of the temperature is less than 1 °C, the temperature randomness has no significant effect on the flexural strength of beams in the wave splashing zone. The flexural strength distribution conforms to the normal distribution in the early stage of service and the Weibull distribution after concrete cracking. Coastal reinforced concrete structures will be affected by chloride ingress, resulting in the decline of load bearing capacity. The process of chloride ingress and steel corrosion involves complex chemical reactions and mass transfer, which is affected by ambient temperature and humidity. Due to the randomness of the variables, it is difficult to accurately calculate the degradation of the structure load bearing capacity. Instead, the probability of the decline of the structure load bearing capacity is more concerned. However, numerous challenges remain in obtaining the probability. For instance, although the probability distributions of basic variables, such as temperature and humidity, have been investigated by many studies, it is very difficult to obtain the probability distribution of load bearing capacity by analytical method because of the complexity of the formulas in calculation. Moreover, it is infeasible to obtain the probability distribution of the load bearing capacity directly by experimental methods, which will cost a large amount of resources. Many studies ,2,3,4,5 The Monte Carlo method has advantages in high-dimensional problems and complex computation, which can calculate complex problems through simple programming and the computation speed is rather fast due to modern computer technology. In this study, the Monte Carlo method was applied to calculate the probability distribution of flexural strength of reinforced concrete beam. In order to obtain the probability model of load bearing capacity, the relationship between basic variables, such as temperature and humidity, and load bearing capacity needs to be established. Moreover, the random distribution of basic variables is required to apply the Monte Carlo method. Collepardi et al.  first usChloride ingress will eventually lead to steel corrosion, which is the main reason for the decline in load bearing capacity. The depth of corrosion (radius loss) and the density of corrosion current are generally used to evaluate the degree of steel corrosion. Bazant and Zdenek  establisIn this paper, based on the studies of influence of temperature and humidity on chloride penetration, the relationship between environment parameters and steel corrosion was deduced, and then the model of load bearing capacity under the influence of temperature and humidity was obtained. The experimental data in different literatures were compared to verify the accuracy of the model. The random distributions of parameters in different literatures were investigated. Through programming to generate random parameters, the corrosion probability and the law of flexural strength decline changes with time were obtained by the Monte Carlo method. The results of temperature and humidity as random variables and constants are compared. The effects of variance of different parameters on the corrosion probability and distribution of flexural strength were discussed. A binary logistic regression formula was proposed to predict whether temperature and humidity randomness should be considered.it, is usually calculated by Fick’s second law as:C is the chloride concentration, D(t) is the chloride diffusion coefficient, x is depth in concrete and t is time.The corrosion of steel bar in concrete under chloride ingress is usually divided into three stages: the corrosion initiation stage, corrosion propagation stage and expansion failure stage. The corrosion initiation stage is the process in which chlorine ions diffuse into the concrete and accumulate to the threshold value of reinforcement depassivation. During this period, it is believed that the reinforcement is not corroded. The duration of corrosion initiation stage, 0D is the chloride diffusion coefficient at time 0t, 0t is the reference time from concrete pouring , D0 is expressed as m is the diffusion decay factor, FA and SG are, respectively, the proportion of fly ash and slag in the mixture. The chloride diffusion coefficient is affected by temperature and relative humidity. In [T,clk is the temperature effect coefficient in chloride ingress, H,clk is the humidity effect coefficient in chloride ingress. For the calculation of T,clk, the effect of temperature on chloride diffusion coefficient can be described by Arrhenius’ law based on the results of [eb is the regression coefficient and is normally distributed. Based on a large number of experimental data statistics, the mean of eb is 4758.47 and the variance is 798.132. 0T is the reference temperature and is usually 20 °C. T is the environment temperature (°C).In Tang and Nilsson’s  and Life+SG/70). , where Fdity. In ,42,43, tsults of ,28:(4)kTH,clk, the humidity effect coefficient is expressed by studies [RH0 is the critical relative humidity which is 0.75. RHc is the relative humidity of environment.For the calculation of  studies ,44 as:, the experimental values in studies ,46,47,48t0 is the age of concrete when chloride ion ingress begins:Equation (1) is a nonlinear partial differential equation. To solve Equation (1), the variable substitution is used as follows where According to the derivation rule of compound function:Equation (9) can be obtained by comparing Equations (1) and (8):C = SC; 2.The concentration of chloride ion in concrete is C0 at the initial time, C = C0; 3.The concentration of chloride ion in concrete is C0 at infinity, C = C0, a special solution can be obtained from Equation (9):Considering three boundary conditions, 1.The concentration of chloride ion on concrete surface is constant, tC, the steel bar in concrete begins to rust. Substituting concrete cover thickness for x, and tC for C in Equation (10), the corrosion initiation time it can be obtained.When chloride concentration reaches the chloride threshold ct. In the standard for durability assessment of existing concrete structures (GB/T 51355-2019) [d is the diameter of steel bar(mm), c is concrete cover thickness (mm), and cu,kf is the standard value of the compressive strength of concrete (MPa).When the steel bar begins to rust for a period of time, the concrete cover will crack due to the accumulation of corrosion products. This period is the concrete cover cracking time, 55-2019) , the forλ is the metal corrosion rate(mm/a), corri is the corrosion current density(μA/cm2), M is the relative atomic mass, n is the metal valence and ρ is the metal density. The following expression of metal corrosion rate before concrete cover cracking can be obtained by substituting the parameters of iron. Steel bars of different strength grades have the same density and metal valence. The corrosion rate can be calculated by the same expression:λ1 is the metal corrosion rate before concrete cover cracking. Liu et al. [slC is the chloride concentration on steel bar surface (kg/m3). T is the temperature (°C). ρ is the concrete resistivity (kΩ·cm). clm is the local environmental coefficient and the value is 4.5~5.5 in the wave splashing zone and outdoor wet-dry cycling environment, and 4.0~4.5 in hot and humid regions. ρK is coefficient. When water to cement ratio is 0.3~0.4, ρK is 11.1. When the water-to-cement ratio is 0.5~0.6, ρK is 5.6. 3). When t after corrosion initiation is:Based on Faraday’s law, the corrosion rate of metal is obtained from corrosion current density:u et al.  obtainedu et al.  proposedu et al. , an equacrδ, the concrete cover will crack. The concrete cover cracking time, ct, can be calculated by substitute (11) into (15):When the average depth of steel corrosion reaches the critical corrosion depth A0 is the initial sectional area of steel bar and corrA is the corrosion area of steel bar. In the present study, the calculation formulas for the section of corroded steel bar mainly include uniform corrosion model and pitting corrosion model. Chloride ions usually reach the steel bar surface quickly through concrete cracks and then adsorb on the local passivation film, which causes the reinforcement serious local corrosion to form the pitting corrosion. Therefore, the pitting corrosion model was introduced in this study to research the section loss of steel bar. In order to obtain the maximum section loss rate, the non-uniform coefficient R is introduced to describe the maximum depth of pitting corrosion:The loss of steel bar section area can be calculated using the corrosion depth. The section loss rate of a steel bar is defined as:max(t)δ can be obtained:By substituting Equation (15) into Equation (18), the expression of R is 4 in this study based on the [2 is the corrosion rate of the steel bar after concrete cover cracking and can be calculated as λ2 < 1.8λ1, λ2 = 1.8λ1 [pA(t), can be calculated from the size of geometry:The value of d on the . The λ2  = 1.8λ1 . The maxyf is the yield strength of steel bar, η is the section loss rate of steel bar. sA is the total section area of steel bars, b is the breadth of beam, x is the height of equivalent rectangular compression zone, 0h is the effective section height, α1 is the graphics coefficient, which is applied to calculate the equivalent stress of concrete in equivalent rectangular compression zone, and usually is 0.8 according to the code for the design of concrete structures (GB 50010-2002) [The ultimate flexural capacity of the reinforced concrete beam under a chloride environment would decrease due to the corrosion of steel bars. By substituting the corroded steel section area for the initial steel section area, the flexural capacity of reinforced concrete beam only with tension reinforcement can be calculated as follows:10-2002) .By simultaneously solving Equation (21), the ultimate flexural capacity of the reinforced concrete beam is obtained:Qingdao is located on the northern coast of China. Taking a rectangular beam with tension reinforcement in Qingdao as an example, the effects of temperature and humidity randomness on flexural strength was calculated by the Monte Carlo method. Material parameters and the beam reinforcement configuration are shown in sC) is different according to the different environment. Three different regions are considered in this paper, which are the wave splashing zone, the coastal atmospheric zone, and the offshore atmospheric zone. The coastal atmosphere is defined as an area above sea level where there is no direct contact with seawater but is affected by salt spray. The offshore atmospheric zone is defined as an area 0.5 km from the coast. Different standards and studies have different recommendations for the value of sC. The values of sC in different standards are listed in 3, which is the chloride ion mass per unit volume of concrete . ACI-365 and JSCE consider the effect of distance from the coast on chloride concentration. ACI-365 suggests that the surface chloride concentration in the coastal zone is accumulated per year and the final value is listed in 3. In the 1990s, Norway [sC of bridge in atmospheric zone would accumulate to the same value of splash zone. The coastal region is zoned simply by DuraCrete [sC ranges from 1.7~18 kg/m3 [sC without the distance from the coast. McGee [sC in study [The main sources of chloride ions on the surface of reinforced concrete structures near the coast are seawater and salt spray. The chloride content of the concrete surface . In this paper, the value of 1.5 kg/m3 is applied in the calculation.The critical threshold concentration of chloride, Tk and Hk values, and, hence, Equation (6) should be changed into the following form:1t, 2t, … nt are different points in time. Tnk and Hnk are different coefficient under different temperature and humidity. Accordingly, the corrosion current density is also a piecewise function. According to the meteorological data of the national surface meteorological observation station, the probability distribution characteristics of the annual average temperature and humidity in Qingdao area for 50 years were calculated. The average annual temperature is normally distributed, the mean is 13.24 °C, and the standard deviation is 0.899 °C. The average annual humidity is normally distributed, the mean is 72.2%, and the standard deviation is 2.355%. The code for acceptance of constructional quality of concrete structures [The randomness of the temperature and humidity and other parameters, such as the concrete cover thickness, affect the flexural strength of reinforced concrete beams. The temperature and humidity are considered constant throughout the service life of structures in the standard for durability assessment of concrete structures. However, many studies suggest that temperature and humidity should be treated as random variables. In this paper, the average annual temperature and humidity are treated as random variables, which means that the temperature and humidity are averaged throughout the year, but vary from year-to-year. Therefore, the temperature effect coefficient and the humidity effect coefficient are piecewise functions of time. Different temperature and humidity values will result in different ructures  stipulatThe environment of coastal structures under chloride ingress in this paper are divided into the wave splashing zone, the coastal atmospheric zone, and the offshore atmospheric zone. The values of RH in the wave splashing zone and coastal atmospheric zone are relatively stable, hence, constant relative humidity was used instead of meteorological data in the calculation of chloride ingress. The values of relative humidity in the wave splashing zone and coastal atmospheric zone are 0.90 and 0.75, respectively.n times at each time point. The corrosion frequency is the number of corrosion beams divided by n. According to Bernoulli’s law of large numbers, the probability can be obtained by the frequency at each time point. The probability converges to a value with the increase of n in the Monto Carlo method. In order to determine a reasonable number of repetitions, the initial corrosion probability of steel reinforcement within five years was calculated with 3000, 4000, and 5000 repetitions. Ten values of probability were calculated with each different repetition numbers. The variance of each group of data with different repetition numbers are shown in In this paper, the Monte Carlo method was applied to obtain the probability of corrosion. For the engineering structure, the designed service life is usually 50 years. In the calculation process, the whole service life was 100 years and was divided into one hundred equal parts and every period was one year. At each year, all the random variables were generated randomly according to the distribution which are shown in pnc is the probability of non-corrosion. pcp is the probability of corrosion propagation. crp is the probability of concrete cover cracking. The sum of the three probability values is equal to 1. From The probabilities calculated by the Monte Carlo method are shown in crp reaches 1 is postponed to the 35th year. The corrosion propagation period starts at five years and ends at about the 40th year, which means the variance of corrosion initiation has increased. This is due to the decrease of diffusion coefficient caused by the decrease of humidity, combined with the decrease of surface chloride concentration. A small change in the thickness of concrete cover can cause a drastic change in the corrosion initiation time, which makes the corrosion initiation time to be more sensitive to the variance of the concrete cover thickness.By comparing the corrosion probability in 3, which is lower than the other two zones. According to Equation (1), the concentration gradient is small and the driving force of diffusion is weak. Therefore, the corrosion initiation time is longer than other zones. The lower RH value is the other important reason. The chloride diffusion coefficient with a RH value of 75% is only half of that at saturation according to Equation (5) [tion (5) ,44. The In order to compare the climatic conditions (temperature and humidity) of different locations, other two typical locations were calculated. Shanghai and Xiamen are two major ports in South China and temperature and humidity are higher than in North China. The average annual temperature and average annual relative humidity of Shanghai are 16.8 °C and 74.3%, respectively. The standard deviation of temperature and relative humidity are 1.103 °C and 2.13%, respectively. The average annual temperature and average annual relative humidity of Xiamen are 21.8 °C and 78.8%, respectively. The standard deviation of temperature and relative humidity are 0.912 °C and 2.54%, respectively. The results are shown in crp of the wave splashing zone with different temperature mean values and standard deviation, and another group of crp,0 which have the same temperature mean values but zero standard deviation, are calculated.The temperature fluctuates randomly in the different regions. When the temperature fluctuation is small, the average temperature can be treated as a constant. In order to understand the effect of temperature fluctuations on the probability of corrosion, the random variables of temperature with different assumptive standard deviations were introduced into the calculation. After the cracking of the concrete cover, the load bearing capacity will decrease rapidly in a short time. Therefore, the probability of concrete cover cracking is most concerned. For beams in the wave splashing zone, the randomness of humidity is not introduced into the calculation because of the RH is relatively stable in the wave splashing zone. The crp is a sequence of values arranged by time and each number represents the probability of cracking of each year in a range from 0 to 1. Hence, the crp can be treated as a multidimensional vector and each dimension is the probability of cracking in a given year. The results of the calculation should be the same each time, but because the Monte Carlo method has a limited number of calculations, there will be small random fluctuations in the results. The Euclidean distance between the two vectors is used to measure the fluctuation as Equation (24). ix and iy represent the members of the two vectors, respectively:The crp and vector cr,p0 is calculated for every combination and is recorded as dist. Every combination is calculated 100 times, forming 100 values of distance, and the mean values are shown in The distance is distributed normally, which is verified by the Lilliefor test. The parameters of the normal distribution are estimated by the maximum likelihood method. The mean is 0.0384 and the standard deviation is 0.01465. This probability distribution of the vector distance between two same calculations is recorded as dist(0)~N. A total of 25 combinations of five different temperature means (8~25 °C) and five different standard deviations (0.5~2.5 °C) were calculated to analyze the effect of temperature randomness. The distance between vector crp and cr,p0 gets larger. When the standard deviation of temperature is greater than 1.5 °C, the alternative hypothesis is accepted. It indicates that the difference between crp and cr,p0 is statistically significant. Therefore, the difference between crp with temperature randomness and those without temperature randomness is insignificant when the standard deviation of the temperature is less than 1.5 °C in the wave splashing zone. In this condition, the temperature can be treated as a constant in the calculation of chloride ingress.From Tμ) are 8, 12, 16, 20, 24 °C. The standard deviations of temperature (Tσ) are 0.5, 1, 1.5, 2, 2.5 °C. The means of RH (RHμ) are 68%, 72%, 76%, 80%, 84%. The standard deviations of RH (RHσ) are 1.5%, 2%, 2.5%, 3%, 3.5%. All of the 625 combinations are shown in RHμ vs. RHσ with a certain combination of mean and standard deviation of temperature. The row and column represent Tσ and Tμ, respectively. H1 means that the calculation results of considering temperature and humidity randomness are statistically different from those without randomness.For the structures in the offshore atmospheric zone, the ambient temperature and humidity fluctuate randomly at the same time. The effect of the humidity randomness should also be considered. The effect of the humidity randomness is considered using the same method as the temperature but the calculation becomes more complicated. Five different means of temperature, standard deviations of temperature, means of RH, and standard deviations of RH, which is a total of 625 combinations, are calculated. The means of temperature . RHμ and RHσ are the mean and standard deviation of the relative humidity (%). P is the probability of y = 1. The value of p can be obtained by substituting the temperature and humidity data of a certain location into Equation (25). When p > 0.5, the alternative hypothesis is accepted which means the randomness of temperature and humidity in this region should be considered.In order to determine the threshold of parameters when the randomness should be considered, a binary logistic regression of 625 sets of data is conducted. The dependent variable, p-value indicates that the distribution fitting against the Weibull distribution has a better goodness of fit.In order to determine that the flexural strength conforms to which probability distribution, the normal distribution, extreme value distribution, and Weibull distribution are used for parameter estimation, respectively. Kolmogorov–Smirnov tests are carried out to verify the goodness of fit. Test results are shown in t < it). In this stage, the corrosion depth is zero due to the algorithm, therefore, the flexural strength remains the same. The distribution conforms to the normal distribution, which is determined by the randomness of the geometric dimension parameters and concrete strength. This period lasts for five years and corresponds to There are three stages in the deterioration process of reinforced concrete structures due to steel corrosion. The first stage is dominated by the penetration of chloride, during which chloride ions diffuse through concrete toward the reinforcing bars . In thisDue to the influence of corrosion, the shape of the flexural strength distribution will change, which can be roughly divided into two stages. It conforms to the normal distribution in the early stage of service and the Weibull distribution after concrete cracking. The influence of temperature randomness on the flexural strength of beam in the wave splashing zone is evaluated. Two groups of flexural strength of 50 years are calculated with and without temperature randomness. The calculation is repeated 5000 times at each time point. Each sample contains 5000 data points. The influence of temperature randomness was verified by the K–S test to determine whether the two samples are from the same population. When the alternative hypothesis is accepted, it indicates that the two samples come from different populations, that is, temperature randomness will cause the change of flexural strength distribution and, hence, the randomness cannot be ignored. Tμ) are 8, 12, 16, 20, 24 °C. The standard deviations of temperature (Tσ) are 0.5, 1, 1.5, 2, 2.5 °C. The means of RH (RHμ) are 68%, 72%, 76%, 80%, 84%. The standard deviations of RH (RHσ) are 1.5%, 2%, 2.5%, 3%, 3.5%. Two groups of flexural strengths are calculated with and without temperature and humidity randomness, respectively. Each group contains 100 years and every time point has 5000 flexural strengths. The K–S test is conducted for two groups of flexural strength to verify whether they are from the same population. This process is applied to every combination. It is difficult to give all the information of the results. Similar to Temperature and humidity change randomly at the same time in the offshore atmospheric zone. Five different means of temperature, standard deviations of temperature, means of RH, and standard deviations of RH comprise a total of 625 combinations to be calculated. The means of temperature (crp with temperature randomness and those without temperature randomness is insignificant when the standard deviation of the annual average temperature is less than 1.5 °C in the wave splashing zone. In this condition, the temperature can be treated as a constant throughout the service life in the calculation of chloride ingress. For the beam in the offshore atmospheric zone, the influence of humidity on the cracking probability is greater than that of the temperature. A binary logistic regression formula is given to predict whether the randomness of temperature and humidity should be considered.The influence of temperature and humidity on chloride ion diffusion was analyzed. The influence of the randomness of temperature and humidity was introduced by treating the annual average temperature and humidity as piecewise functions. Taking temperature, humidity, geometric size, and concrete strength as random variables, the corrosion probability and the distribution of flexural strength of a typical design beam was obtained by the Monte Carlo method. Results show that the corrosion probabilities of the wave splashing zone, coastal atmospheric zone, and offshore atmospheric zone are very different. This is mainly due to the difference in the concentration of chloride ions on the surface. The RH value significantly affects the diffusion coefficient and the corrosion probability of the offshore atmospheric zone is less than that in other two zones at the same time. The randomness of temperature and humidity will affect the cracking probability. The difference between The flexural strength decreases rapidly after concrete cover cracking. The flexural strength distribution conforms to the normal distribution in the early stage of service and Weibull distribution after concrete cracking. When the annual average temperature standard deviation is less than 1 °C, the temperature randomness has no significant effect on the flexural strength of beams in the wave splashing zone. The period during which the temperature randomness makes a difference is usually in the middle of service. For the beam in the offshore atmospheric zone, the temperature and humidity randomness should be taken into account in the calculation of chloride ingress. This method can be used to predict the corrosion probability and the flexural strength of the beam or other reinforced concrete member of a given design size.


Complex question answering in real world is a comprehensive and challenging task due to its demand for deeper question understanding and deeper inference. Information retrieval is a common solution and easy to implement, but it cannot answer questions which need long-distance dependencies across multiple documents. Knowledge base (KB) organizes information as a graph, and KB-based inference can employ logic formulas or knowledge embeddings to capture such long-distance semantic associations. However, KB-based inference has not been applied to real-world question answering well, because there are gaps among natural language, complex semantic structure, and appropriate hypothesis for inference. We propose decoupling KB-based inference by transforming a question into a high-level triplet in the KB, which makes it possible to apply KB-based inference methods to answer complex questions. In addition, we create a specialized question answering dataset only for inference, and our method is proved to be effective by conducting experiments on both AI2 Science Questions dataset and ours. Teaching machines to answer complex questions like human beings is a very challenging task at the intersection of nature language processing (NLP), information retrieval (IR), and artificial intelligence (AI), which mainly needs three techniques, i.e., question understanding, answer retrieval, and inference. There are three subtasks specifically to evaluate the corresponding techniques: Question Answering over Knowledge Base (KBQA) is a typical task to evaluate question understanding; Text Retrieval Question Answering (TREC QA) and Reading Comprehension (RC) are good tasks to evaluate answer retrieval and answer selection; Link Prediction and Knowledge Base Completion (KBC) are traditional tasks to evaluate inference.To attract bees for pollinationTo create flower arrangementsTo protect the tree from diseaseTo feed migratory birdsAfter achieving progress in these subtasks, researchers begin to turn their passion to more comprehensive and complex question answering (QA) tasks. Allen Institute for Artificial Intelligence (AI2) proposes a science test which is a real-world examination for elementary students and middle school students, and it is even viewed as a standardized measure of AI. An example question in a science test is shown as follows. Q1: Peach trees have sweet-smelling blossoms and produce rich fruit. What is the main purpose of the flowers of a peach tree? (Answer is A.)Such complex questions are hardly solved by any single technique mentioned above, and it is also difficult to effectively combine these techniques, which makes the task far from solved.Retrieval can get effect instantly on a question whose answer (or some keywords inside) is near the question in background corpus, and the method achieved the best performance on 8th Grade Science Challenge . HoweverX), and its correct answer is expected to be Attract  ∧ Do . However, it is difficult to obtain such precise structures, because the quality of semantic parsing is far from satisfactory [There is a gap between a natural language question and the semantic structure in KBs. Semantic parsing is used to transform a natural language to the semantic structure on the KB; e.g., for Q1, the question is expected to be Purpose  with the candidate answer  ∧R2  ∧ …⇒ Purpose  ∧ Do )), which is far from frequent in KBs and difficult to be found by formula learners.Even if the question can be precisely transformed into a structure by semantic parsing, complex structure in KBs is not appropriate as a hypothesis for inference. In inference, a hypothesis is a candidate proposition which needs to be proven by evidence; e.g., we substitute nswer as  shows, aOn the other hand, knowledge bases (KBs) are graph-structured background data which contain vast long-distance semantic associations. Inference on KB is expected to capture such long-distance semantics as evidence by logic formulas , 4 and kR  ∧ ?R  ∧ Do . However, after unfolding and dividing, some structural information in the original question is missing, which embodies two aspects:There are no explicit relations between some entities. After unfolding, the original relation between the question and the answer  ⇒ Do  which supports Do  is irrelevant with Has . Actually, there should be associations among these hypotheses, and it is these associations that make atomic hypotheses the original question.An intuitive solution to complex hypothesis is to unfold the recursive structure and divide it into several atomic hypotheses; e.g., the structure in For the entity pairs which have no explicit relations, we create a virtual relation Rq to replace unknown relations of entity pairs, and represent Rq into distributed semantic space according to the linguistic expressions of the question. We expect different dimensions of Rq can capture relations of different entity pairs. For example, Rq  and Rq  focus on different aspects of Rq.H, T) to combine all possible atomic hypotheses, and adapt original KB-based inference methods to infer it. For example, atomic hypotheses in h ∈ H and entity t ∈ T. For example, in h, x) ∧ Feed  ⇒ Rq , which is an evidence for the virtual hypothesis. At last, we build a joint inference model to eliminate irrelevant or noisy evidence (including formulas and embeddings) which may be introduced by meaningless entity pairs, e.g., Peach Tree, Pollination irrelevant to the original question.For the atomic hypotheses among which there are no associations, we create a virtual hypothesis Rq , where questions that can be answered only by simple retrieval have been filtered out. The experimental results on InfQAD show that logic inference and embedding-based method concentrate on different aspects of questions and they can complement each other.  We decouple KB-based inference from question answering by creating a virtual hypothesis and apply inference to answer complex questions, which not only utilizes long-distance semantic associations but also bridges the gap between natural language questions and hypotheses for inference.  We create a new dataset from real-world examination questions to specifically evaluate the performance of inference methods on those complicated questions which need inference to resolve. It contains seven subsets of different subjects and may promote study on domain specific inference.   We conduct an experiment on AI2 Science Questions Dataset to prove that inference can improve the performance of retrieval. After that, we compare several inference methods on InfQAD, and our method outperforms baselines.In summary, the contributions of this paper are shown as follows:r , and the evidence can be a path, loop, or subgraph. There are mainly two types of models: probabilistic logic inference and knowledge graph embedding.An inference task should contain a hypothesis and evidence, and inference is a process of collecting evidence to prove the hypothesis. For simple inference task on the KB, the hypothesis usually has the form of x, y) ∧ Feed  ⟹ Attract  is a formula which can infer Attract , and its head is Attract , and its body is an abstraction of evidence, Has  ∧ Feed .Probabilistic logic inference utilizes various logic formulas to perform probabilistic inference. A logic formula has a head and a body, respectively, corresponding to hypothesis and evidence, noted as Body ⟹ Head. For example, Has  . Random H  as input and starts random walks from the head entity x in each ground hypothesis (Lines 1–3). For example, the algorithm finds a path from Flower to Bee for Attract , e.g., x, y) ∧ Feed . After obtaining weighted formulas (Lines 6-7), probabilistic inference model employs the formulas as features and estimates the probability of the hypothesis asxi is a value about fi, e.g., truth value or count, and the denominator is a normalizing constant. φ is a nonlinear function; e.g., ϕ is an exponential function in Markov Logic Network.The algorithm takes a class of hypotheses Fr; e.g., TransE [Eh, Et, Er are the embeddings of two entities and one relation, respectively. At training, the score of the triplet in KB is expected to be larger than triplets not in KB. After multiple rounds, embeddings are considered to contain implicit semantics and can perform inference.Knowledge graph embedding (KGE) model represents entities and relations as low-dimension numeric vectors or tensors and expects that arithmetical operations among embeddings can capture implicit relationships among elements. KGE can apply to inferring the hypothesis by defining a score function, noted as , TransE  defines r  in the KB as a hypothesis for inference, so if a complex question can be transformed into a triplet, KB-based inference method can solve it. This section describes a method that can distill a high-level triplet from the question as the hypothesis, and we call it virtual hypothesis.The above KB-based inference methods all take a triplet Rq and we propose the first assumption here.We try to transform a pair of question and option into a high-level triplet Rq for a pair of question and option is the combination of all possible triplets r , where r is an implicit relation between an entity h in question and an entity t in option. Thus, h ∈ H, t ∈ T, and Rq is an integration of r.The virtual hypothesis r  is true, h+r ≈ t, and the distance of r  in distributed space, Dr=‖Eh+Er − Et‖, should be close to zero. We represent the sum of distances for all possible triplets as Dq, e.g., triplets in Dq=∑i=1n‖Ehi+Eri − Eti‖. According to Triangle Inequality, ‖∑i=1nEhi+∑i=1nEri − ∑i=1nEti‖ ≤ Dq. However, ri may have no clear definition; e.g., the relation between Flower and Pollination in Rq and set ERq=∑i=1nEri, so the distance of Rq is DRq=‖EH+ERq − ET‖, and DRq ≤ Dq which means the virtual hypothesis Rq is true if and only if all atomic hypotheses ri are true. Thus, we believe the virtual hypothesis covers all semantics in atomic hypotheses, but there is temporarily no association between the virtual hypothesis and the original question. We propose the second assumption as follows.We employ TransE model to explain the correction of Rq is close to the embedding of the original question without entities.In distributed space, the embedding of virtual relation Rq is a combination of implicit relations between question entities and option entities, which should be what the question describes. For example, a simple question “Who is first emperor of Tang Dynasty” can be represented by a triplet FirstEmperor , and then “Who is first emperor of” describes the semantics of the relation type FirstEmperor. Therefore, Rq—H and T, respectively, are the entity sets in question and option, and Rq is the question without entities. At present, we have distilled a high-level triplet from a pair of question and option as the simple hypothesis for inference.According to r  on the KB, we select applicative logic formulas by the relation r; e.g., the formula Has  ∧ Feed  ⇒ Attract  which is obtained from the instance Has  Feed  ∧ Attract  can be also used to infer Attract . However, the virtual relation Rq is specific to a question, and no two Rq can share their formulas. To capture associations between Rq  and formulas, we propose the third assumption.When we employ logic formulas to infer a normal triplet f can be used to infer Rq, the body of f should be close to Rq in distributed space.If a formula x, y) ∧ Father  ⇒ Grandfather , Father + Father should be close to Grandfather. More formally, we still employ TransE to explain the correction. For a formula f, r1∧…∧rn⇒r1, if the body of f is true, and ri is true. Thus, Exi+Eri=Exi+1, and then Exi+∑i=1nEri=Ern+1. We define the embedding of f as Exf=∑i=1nEri, so Ex1+Erf=Exn+1. Since the head of f is also true, Ex1+Err=Exn+1, so we get Ef=Er, which is exactly Intuitively, some formulas have such a property; e.g., in Father  to replace the count of f 's instances between H and T, so equation , in which questions cannot be answered by search or retrieval. After that, we compare several types of inference on InfQAD.AI2 Science Question Dataset contains 5343 4-way multiple-choice science questions without diagrams at the elementary and middle school levels, and they are divided into Train, Dev, and Test. For each solver, we normalize scores across the answer options for a given questionWe send normalized scores into a classifier which can output correct/incorrect with confidence, and the correct option with the maximal confidence is treated as the final answerWe implement a retrieval method based on Lucene which is an open-source information retrieval software library, and we employ the method to build reverse index on the whole Wikipedia dump. We concatenate a question with an option as the query for retrieval and calculate the average of Top-3 scores. We rank options by the average scores, and the highest one is the final answer. All questions and options are preprocessed by CoreNLP . For ourCombining two types of inference methods with retrieval can improve performances, which proves that decoupling inference by virtual hypothesis is effective and KB-based inference can utilize a mass of extra long-distance knowledge to improve the performance of the retrieval method.The promotion on middle school dataset is more obvious than that on elementary dataset, which implies middle school examination is more difficult than elementary examination, and difficult questions need inference more.Only adding embedding into retrieval leads to performance reduction. We think the reason is that wrong answers from the unsuitable solver may affect others. Logic inference tends to refuse to answer with low confidence, while embedding method gives answers in any case, which may distract retrieval from giving the correct answer.We show the accuracy of methods in Retrieval-based method achieves a good performance on AI2 dataset, but the experiment above shows that retrieval may affect the further exploration of inference. Therefore, we propose constructing a new Inference Question Answering Dataset, named as InfQAD, which only contains complicated questions that need inference. InfQAD contains 11,393 examination questions in seven subjects with two languages (five subjects in English and two subjects in Chinese), and in the dataset, questions that only need retrieval have been filtered out. For five subjects in English, we download questions from the CK12 website2. There is a downloadable quiz in almost every topic, and the quiz usually contains ten questions. We only keep 4-way multiple-choice questions without diagrams as AI2 does. For two subjects in Chinese, we collect about 200 senior high school entrance examination papers, which also only keeps 4-way multiple-choice questions without diagrams.To filter out questions which can be answered by retrieval, we treat Lucene as a standard retrieval method and employ it to score each pair of question and option. We sort questions according to the difference between the score of its correct answer and the maximal score of its incorrect option in descending order. We remove top questions and make the accuracy of Lucene on the rest of questions 25% which equals the accuracy of random choice. We believe Lucene fails for the rest of the questions, and they can be viewed as questions that need inference to resolve, approximatively.Traditional MLN , which tCluster-based MLN, which first clusters questions by the similarities between questions and then trains an MLN Model for each cluster of questionsOur method described in We compare different kinds of methods on InfQAD, including probabilistic logic inference in , embeddiEmbedding-based inference contains two approaches which both estimate the similarity between questions and options but employ different methods of representing text, i.e., SUM  and GRU We implement MLN as described in . We implComparing VHLogic with other logic inference methods in Tables Comparing logic inference methods in Comparing ensemble inference methods in We show the accuracy of methods in Tables Complex Relation. This category is that there is a relationship among more than two entities in the question, and the relationship is the key to answer the question. This category is the largest category, and 26% of questions belong to it.Missing Entity from KB. This category is that there is a key entity missing from the KB, which leads to key formulas not being found. This category contains 22% of questions.No Entity in Answer. This category is also about entities and contains 5% of questions. Answers of these questions contain no entity but numbers, modifications, or other elements.No Formula. Sometimes, there are still no paths or useful formulas between entities, though entities in the question and the answer are both linked in KB. 13% of questions belong to this category.Irrelevant Formulas. Irrelevant formulas are noise in inference process, which may disturb useful formulas, and this category has 14% of questions.Key Modifier. Comparative degree, superlative degree, and other modifiers may be the key of answering the question, but these modifiers cannot be captured by VHLogic. There are 8% of questions belonging to this category.To analyze various causes of breakdowns, we sample 100 questions answered incorrectly by VHLogic and roughly classify them into several categories shown in :Complex There are other categories: 7% of questions belong to Math category which needs mathematical operations. 4% of questions need Global Information; e.g., the answer is All of the above, and there are 1% of questions with Wrong Answer.Our work is related to two types of work: question answering and KB-based inference. In recent years, various QA tasks and datasets have been emerging in an endless stream. WebQuestions , BAbI 1, and SimOn the other hand, knowledge base (KB) organizes information as a graph. Graph learning has been widely used in many other fields such as image classification , 24. KB-We propose a method to decouple KB-based inference from real-world QA by creating a high-level triplet on the KB named as virtual hypothesis, and adjust logic-based and embedding-based method to inferring it. The experimental results prove that our method is effective and is a promising method to apply inference to QA. In addition, we propose a specialized question answering dataset only for inference, named as InfQAD. We compare various inference methods on InfQAD and find that different types of inference are skillful in different subjects and combing them will improve the performance. At last, we analyze various causes of breakdowns, which can be helpful for the future study on domain specific inference. In the future, there are two aspects of our work that need deeper exploration. We try to find a better way to represent virtual hypotheses and try to reconstruct textual knowledge base to better capture long-distance evidence as formulas.


The idea of this work was to introduce effective methods of production of these composites that could be used for future biomedical applications such as antibiotics, tissue regeneration, anticancer therapy, or bioimaging. In order to characterize the pristine graphene materials and resulting composites, we used spectroscopic techniques: XPS and Raman, microscopic techniques: SEM with and AFM, followed by X-Ray diffraction. We obtained volumetric composites of flake graphene and Ag, Au, Ag2O, and TiO2 nanoparticles; moreover, Ag nanoparticles were obtained using three different approaches.Graphene oxide (GO) and reduced graphene oxide (RGO), due to their large active surface areas, can serve as a platform for biological molecule adhesion (both organic and inorganic). In this work we described methods of preparing composites consisting of GO and RGO and inorganic nanoparticles of specified biological properties: nanoAg, nanoAu, nanoTiO In the last decade, the properties of graphene materials have been intensively studied in the scientific community in order to develop their potential in various biomedical applications. Graphene is a single layer (in thickness) of carbon atoms arranged in hexagonal rings of an aromatic electron structure. Graphene oxide and reduced graphene oxide are the graphene derivatives characterized by the presence of oxygen functional groups on the graphene surface (more in graphene oxide (GO) than in reduced graphene oxide (RGO)) and, therefore, by the presence of defects in the graphene hexagonal rings as well. Such disorder in the graphene structure leads to many unique properties of GO and RGO.Due to the presence of oxygen groups bonded to the surface, GO is an electrical insulator . GO is highly hydrophilic and therefore has great wettability. The surface of both GO and RGO is susceptible to modifications with different molecules, including biological ones, which allows composites with strictly controlled and desired properties to be synthesized. GO and RGO, due to their large active surface areas, can serve as a platform for biological molecules (both organic and inorganic) to be safely introduced into the organism without the risk of uncontrolled and undesirable spreading into the surrounding tissues. This is a great advantage, for example, in the case of anticancer therapy, where cytotoxic compounds are introduced into the tumor ,2,3,4,5.In many biomedical applications, it is crucial to use materials that are nontoxic to human cells, in terms of this requirement GO and slightly reduced GO seem to be suitable materials. The biocompatibility of these two graphene derivatives has been confirmed by many publications in the literature . This as4) as an oxidant to the reaction mixture in the presence of concentrated sulfuric acid (VI) and NaNO3 salt. Marcano and Tour proposed an improved method of graphite oxidation synthesis involving the elimination of sodium nitrate (V) from the reaction mixture, increasing the amount of potassium manganate (VII) and carrying out the reaction in the presence of concentrated acids: H2SO4 and H3PO4 in a volume ratio of 9:1. As a result, GO with a higher content of oxygen functional groups can be obtained. On the basis of these methods, numerous modifications are made to the synthesis of graphene oxide: partial oxidation to prepare GO of C/O atomic ratio between 12 and 3 ), sodium borohydride (Ag[BS]) and polyphenols from green tea (Ag[PP60]).Three different reducing agents were used to reduce AgTo prepare the composite of GO with nano-Ag obtained with L-ascorbic acid (GO Ag[KA]), a silver nitrate (V) solution was added to the aqueous suspension of GO while stirring and after a few minutes an aqueous solution of L-ascorbic acid was added dropwise. The reduction mechanism is shown in The RGO composite with Ag[KA] (RGO-Ag[KA]) was prepared using the above recipe for GO-Ag[KA], followed by the addition of L-ascorbic acid to finally reduce the GO. The reaction was performed for 3.5 h at 95 °C. The GO: L-ascorbic acid molar ratio was 1:4. After the reaction was completed, the material was purified by dialysis. The two-step process was carried out to obtain a good distribution of silver nanoparticles on RGO flakes. Because GO flakes are well dispersed, firstly, nano-Ag was precipitated on these non-agglomerated flakes. After that, the GO reduction causing also partial agglomeration of the flakes was conducted (with the use of ascorbic acid and the higher temperature) without negative influence on the distribution of the nanoparticles on RGO flakes.A reaction between silver nitrate and sodium borohydride in a water solution was described by Sobczak-Kupiec et al. (2011) . In brie4 creates a reductive and alkaline environment, causing deprotonation of carboxylic groups, which renders more negative zeta potential and therefore improves the GO flakes stability  composite, silver nitrate (V) was added successively to the aqueous GO suspension, followed by sodium borohydride. The molar ratio of GO (MC=C40 g/mol) to silver was 1:0.08 and the molar ratio of silver nitrate (V) to sodium borohydride was 1:2. NaBHtability . Howeverese ions .The reaction was continued for 24 h at a room temperature with continuous stirring. The material was then dialyzed for 72 h to remove the remaining ions.The RGO-Ag[BS] composite was obtained through the preparation of the GO-Ag[BS] composite followed by the addition of L-ascorbic acid to reduce the GO. The reaction was carried out for 3.5 h at 95 °C. The GO: L-ascorbic acid molar ratio was 1:4. After the reaction was completed, the material was dialyzed for 72 h.3 solution was added to the aqueous suspension of GO at a molar ratio of 1:0.08. Then, the polyphenol PP60 solution was added dropwise in a 1:1 molar ratio with AgNO3. The reagents were vigorously stirred on a magnetic stirrer for 24 h at room temperature. The material was purified by dialysis.To obtain the GO-Ag[PP60] composite, an aqueous AgNOThe procedure for obtaining the RGO composite with Ag[PP60] nanoparticles consisted of producing the GO-Ag[PP60] composite (as described above) and adding L-ascorbic acid to reduce the GO. The reaction was carried out for 3.5 h at 95 °C. The GO:ascorbic acid molar ratio was 1:4. After the reaction was completed, the material was dialyzed.4·3H2O was added dropwise to the GO water suspension at a GO:HAuCl4 molar ratio of 1:0.08. The mixture was stirred on a magnetic stirrer for 30 min at room temperature (to obtain a homogeneous mixture of GO flakes and Au3+ ions), after which the temperature was increased to 80 °C to provide the conditions needed for Au3+ reduction. Then, an aqueous solution of sodium citrate (3-hydrated) was added dropwise at a molar ratio with HAuCl4 of 1:0.17. The reaction continued for 1 h at 80 °C while being stirred. A composite with a purple glow was formed, proving the formation of gold nanoparticles. The material was dialyzed to remove the remaining ions. The mild reaction conditions did not reduce the GO, only Au3+ to Au0.To prepare the GO-Au composite, an aqueous solution of HAuClTo prepare the RGO-Au composite, L-ascorbic acid was added to the previously prepared GO-Au composite. The reduction was carried out on a magnetic stirrer for 3.5 h at 95 °C and purified by dialysis.2O composite was obtained by adding silver nitrate (V) solution followed by sodium hydroxide solution to the aqueous GO suspension. This order of adding compounds was used because by adding NaOH first would cause GO agglomerates to form  and the Ag nanoparticles to be poorly distributed on the flakes. The addition of AgNO3 to GO before NaOH provides less alkaline conditions due to the presence of strong acid salt. The molar ratio of GO to silver nitrate (V) was 1:0.08 and the molar ratio of silver nitrate (V) to sodium hydroxide was 1:1. The reaction was carried out at room temperature for 24 h with continuous stirring. The resulting material formed a stable aqueous suspension that was dialyzed to remove residual ions.The GO-Ag2O composite was produced by reducing GO with L-ascorbic acid as described in 3 and NaOH was then added to RGO to precipitate Ag2O nanoparticles. The reactions were carried out at room temperature for 24 h followed by purification with dialysis.The RGO-Ag2 composite, titanium isopropoxide was added dropwise to a small amount of absolute ethanol and then added slowly to the aqueous GO suspension under vigorous stirring. During the addition of GO, a white TiO2 precipitate formed. Proper amounts of the compounds were used to obtain a molar GO:TiO2 ratio of 1:0.08.To obtain the GO-TiO2 composite was prepared by reducing the GO with L-ascorbic acid and purifying it by dialysis. The appropriate amount of titanium isopropoxide (standard 1:0.08 molar ratio) was then added dropwise to a small amount of absolute ethanol, after which it was added slowly to the RGO suspension under vigorous stirring. The sample was stirred for 24 h in order to precipitate out TiO2 particles.The RGO-TiOThe images from the SEM  show theThe working principle of AFM—based on mechanical contact between the microscope’s scanning probe and tested surface allowed for the quantification of average thickness of GO and RGO flakes. AFM was operating in tapping mode, which enabled an acquisition of high resolution topographical images, from which the flake thickness was estimated. The average GO flakes thickness was estimated at 1.2 ± 0.2 nm. Knowing that the thickness of pure graphene  is between 0.4–1.7 nm , and takThe XPS survey spectra  and the 2 (C=C) and sp3 (C–C) hybridization, hydroxyl/phenyl (C–OH), epoxy ([CC] > O), ether (C–O–C), carbonyl (CC=CO), and carboxyl (O–C=O). Carbonate ions (CO32−) and defective carbon structures have also been detected. 2) band intensity to those corresponding to oxygen groups—especially hydroxyl (C–OH), epoxy ([CC] > O), and ether (C–O–C). In RGO, as a result of the reduction, the C=C double bond structure was partially restored: from 10.22% for GO to 43.70% (4.3-fold increase). The XPS measurement shows that functional oxygen groups were less frequent in RGO than GO. Moreover, the sum of ether and epoxy groups in GO was 41.82%, while in RGO it was 10.40%. The results of XPS spectroscopy confirmed that GO flakes were successfully reduced using L-ascorbic acid.The spectra from the C1s region of GO and RGO display very significant differences, mainly concerning the ratio of C=C (spThe GO and RGO powders obtained by freeze-drying were characterized with the XRD method. In −1 (D band) and about 1600 cm−1 (G band), respectively. The G peak corresponds to the sp2 hybridization of the carbon network and is attributed to the first order scattering from the E2g phonon modes in the Brillouin zone. Moreover, it originates from the stretching of the sp2 carbon pairs in both the rings and chains  indicating the presence of carbonates, which is the result of exposing the material to atmospheric conditions [−1, corresponding to the stretching vibrations of the COO groups and phenyl rings [−1 [2O at above 400 cm−1 can be attributed to the stretching vibrations (Ag-O) of the nanoparticles [2 particles indicates the presence of nuclei of the anatase (A) and rutile (R) phases; the respective bands are marked on The Raman spectra  for all nditions ,53. The yl rings . Based oings [−1 . The banarticles . The RamEDS measurements  made of To visualize the morphology of pristine nanoparticles, they were produced without the presence of GO/RGO. However, the synthesis conditions  were kept as for the respective synthesis conducted in the presence of GO/RGO.The SEM images in The SEM images  indicate2O was also examined by the SEM technique. The images indicate that the grain size of the material was 200–400 nm, but smaller dots can also be observed. The resulting SEM images of composites show that nano-Ag2O particles have good distribution on both the GO and the RGO flakes, creating small nanometer-sized dots and larger agglomerates, especially in the RGO. Based on the SEM images, it can be estimated that the diameter of the TiO2 particles is well below 50 nm. The nano-TiO2 particles created a very good distribution on the GO and RGO flakes—they did not form agglomerates and were uniformly present in the entire volume of composites.The morphology of the nano-Ag2O, and TiO2—of a known biological activity. Moreover, three different routes of silver nanoparticle synthesis were compared in the creation of the desired homogenous composites with flake graphene. The forms of graphene used (graphene oxide and slightly reduced graphene oxide) are known to be nontoxic towards human cells, for example, mesenchymal stem cells, a finding that was confirmed in our previous work [In this paper we described in detail the effective methods of preparing composites consisting of graphene flakes (GO and RGO) and inorganic nanoparticles—Ag, Au, Agous work . This ma


Motor neuron disease (MND) is a progressive neurodegenerative disease with no effective treatment. One of the principal pathological hallmarks is the deposition of TAR DNA binding protein 43 (TDP-43) in cytoplasmic inclusions. TDP-43 aggregation occurs in both familial and sporadic MND; however, the mechanism of endogenous TDP-43 aggregation in disease is incompletely understood. This study focused on the induction of cytoplasmic accumulation of endogenous TDP-43 in the motor neuronal cell line NSC-34. The endoplasmic reticulum (ER) stressor tunicamycin induced casein kinase 1 (CK1)-dependent cytoplasmic accumulation of endogenous TDP-43 in differentiated NSC-34 cells, as seen by immunocytochemistry. Immunoblotting showed that induction of ER stress had no effect on abundance of TDP-43 or phosphorylated TDP-43 in the NP-40/RIPA soluble fraction. However, there were significant increases in abundance of TDP-43 and phosphorylated TDP-43 in the NP-40/RIPA-insoluble, urea-soluble fraction, including high molecular weight species. In all cases, these increases were lowered by CK1 inhibition. Thus ER stress signalling, as induced by tunicamycin, causes CK1-dependent phosphorylation of TDP-43 and its consequent cytosolic accumulation.The online version of this article (10.1007/s11064-019-02832-2) contains supplementary material, which is available to authorized users. Motor neuron disease  is a progressive, incurable and ultimately fatal neurodegenerative disease . OngoingTARDBP gene (encoding TDP-43), the disease-causing mutations cluster in the TDP-43 C-terminal region [C. elegans have been discovered which improve viability [One of the key pathological hallmarks of MND  is the presence of TDP-43 inclusions in motor neurons , 10, altiability –16. It iiability –19.The mechanisms underlying TDP-43 aggregation in sporadic MND are incompletely characterized, although phosphorylation of TDP-43 is likely to be important . TDP-43 4C2 hexanucleotide expansion found in the C9ORF72 non-coding region, which also exhibit TDP-43 deposition [While the specific pathological drivers of sporadic MND are largely uncharacterized, TDP-43 inclusions are somewhat reminiscent of stress granules (SGs) —many celposition , 35. Howposition , 36, 37.position , but TDPposition , indicatER stress and subsequent downstream signalling have been strongly linked to MND –42. The In this study, we show that the ER-stressor tunicamycin induces the accumulation of endogenous TDP-43 into cytoplasmic inclusions. When tunicamycin treatment is administered to NSC-34 cells alongside a CK1 inhibitor, TDP-43 cytoplasmic accumulation is markedly reduced, in addition to its phosphorylation and segregation into the RIPA-insoluble/urea-soluble fraction, which suggests that CK1 plays a role in the mislocalisation of TDP-43 observed under conditions of ER stress.All chemicals were purchased from Fisher Scientific  unless otherwise stated. Pharmacological inhibitors were sourced as indicated: tunicamycin (Cat T7765), PF670462 (Cat SML0795) , sodium arsenite (Cat #12897692), sorbitol (Cat #10396733) (Fisher Scientific), and D 4476 (Cat CAY13305) . Dimethyl sulphoxide was used as a vehicle in cell culture treatments.Primary antibodies used were for the TDP-43 N-terminus  or 1:1000 (IB)), Phospho(409/410)-TDP-43 , BiP/Grp78, , TIAR , G3BP1  and β-actin .l-glutamine (Cat #35050061), 100 U/mL penicillin, and 100 µg/mL streptomycin (combined Cat #15140122)  to a maximum of passage 30. Cells were incubated at 37 °C and 5% CO2 in a humidified atmosphere. For differentiation, cells were seeded and cultured in full growth medium (above) for 24 h, then differentiated for 7 days in DMEM-F12  supplemented with 1% FBS, 100 U/mL penicillin, 100 µg/mL streptomycin, 1% non-essential amino acids and 1 µM retinoic acid (Cat R2625) (Sigma-Aldrich) [Motor neuron-like NSC-34 cells   were culAldrich) , 53. Forg for 5 min (4 °C) and re-suspended in 6 × volume of lysis buffer (RIPA buffer: 50 mM Tris–HCl (pH 8.0), 150 mM sodium chloride, 1% Igepal CA-630 , 0.5% sodium deoxycholate, 0.1% SDS, 1 mM sodium fluoride, 1 mM sodium orthovanadate, and Complete Protease Inhibitor cocktail ). Lysis was performed for 30 min on ice, followed by centrifugation at 100,000×g for 30 min (4 °C) to yield the RIPA-soluble fraction as the supernatant. The resultant insoluble pellet was washed in RIPA buffer, re-centrifuged and resuspended in urea-containing buffer (30 mM Tris–HCl (pH 8.5), 7 M urea, 2 M thiourea, 4% CHAPS, 1 mM sodium fluoride, 1 mM sodium orthovanadate, and Complete Protease Inhibitor cocktail) and sonicated, followed by centrifugation at 100,000×g for 30 min. The supernatant was taken as the (RIPA) insoluble fraction.Separation of the RIPA-soluble and urea-soluble fractions was performed as previously described . In brieProtein concentration in the RIPA-soluble fraction was determined using the bicinchoninic acid (BCA) method , using aProtein samples were separated by electrophoresis (120 V for 90 min) on a polyacrylamide gel containing 12% acrylamide in the absence of reducing agent. After SDS-PAGE, proteins were transferred to polyvinylidene fluoride (PVDF) membranes for 75 min at 125 V (Bio-Rad). The PVDF membranes were incubated for 2 h in blocking solution (5% (w/v) milk powder, 2% (w/v) BSA in TBS + 0.1% (v/v) Tween-20 (TBST)) and then incubated overnight in primary antibody (5% (w/v) milk powder (or BSA for phosphorylated epitopes), in TBS). The PVDF membranes were washed 4 × 10 min with TBST before the addition of secondary antibody  milk powder in TBST, 1:5000 (Thermo Fisher Scientific)) for 1 h, followed by 4 × 10 min washes with TBST. Protein bands were visualized by chemiluminescence  using a G:BOX and GeneTools software .NSC-34 cells were lysed in minimal lysis buffer (50 mM Tris (pH7.4), 1% Triton X-100 and 0.5% sodium deoxycholate, followed by incubation with λ phosphatase  according to the manufacturer’s instructions followed by SDS-PAGE as described.Cells were cultured on gelatin coated glass coverslips . After treatment, cells were fixed in 4% paraformaldehyde for 10 min then washed in PBS. Cells were subsequently permeabilised in 0.5% Triton X-100, washed in PBS and incubated with blocking buffer  in PBS) for 1 h. Coverslips were incubated with primary antibody for 1 h , washed in PBS and incubated for 1 h in secondary antibody . After washing, coverslips were incubated with 1 µg/mL DAPI  for 5 min and mounted on microscope slides with Prolong Diamond mounting medium . Images were collected on a Zeiss Axioimager.M2 upright microscope using 20 × plan apochromat or 63x plan apochromat objectives as indicated and captured using a Coolsnap HQ2 camera  through Micromanager software v1.4.23. Specific band pass filter sets for DAPI, FITC and Texas Red were used to prevent bleed through from one channel to the next. Images were then processed and analysed using ImageJ .For cell viability assays, NSC-34 cells were cultured in 96-well plates. An aliquot of culture medium was extracted and added to an equivalent volume of lactate dehydrogenase reaction buffer , incubated for 30 min and absorbance read at 490 nm using a plate reader. The cells and remaining medium were incubated with CellTiter 96 AQueous One or Cell Titer Glo  and, respectively, absorbance at 490 nm or luminescence recorded.t test (control versus tunicamycin) or one-way ANOVA (multiple groups) with Tukey’s post hoc test; p < 0.05 (*), p < 0.01 (**) p < 0.001 (***) or p < 0.0001 (****). All statistical analyses were performed using GraphPad Prism 7 . D’Agostino Pearson normality testing was performed which showed data  resulted in generation of TIAR- and also G3BP1-positive SGs. There were multiple granules per cell, producing an exclusively cytosolic speckled pattern, with a broadly even distribution throughout the cytosol. However, treatment with sodium arsenite failed to generate TDP-43-positive inclusions  and immunoblotting were used as previously reported .Immunoblot analysis of the RIPA-soluble fraction showed no significant changes in either TDP-43 or phospho-TDP-43 (S409/S410) (pTDP-43) immunoreactivity with tunicamycin treatment ± CK1 inhibitors . This finding suggests that our system may be modelling the early stages of sporadic disease, showing that inclusions of TDP-43 are not initially concurrent with cell death or metabolic disturbance. This is consistent with the notion of TDP-43 accumulating as part of a protective response to stress, analogous to the role of stress granules. However, if the concentration of tunicamycin is raised to 0.5 µM or above, then the cells exhibit substantial deficits in their ability to metabolise the MTS tetrazolium dye. Notably, cells still do not exhibit signs of frank cell death (i.e. membrane permeabilisation), even at the highest concentrations of tunicamycin. Co-incubation of cells with a CK1 inhibitor shows this to have a beneficial effect at the earliest stages, i.e. blocking TDP-43 phosphorylation and cytosolic accumulation. However, once metabolic disturbances occur at higher concentrations of tunicamycin, kinase inhibition has no effect. This paradigm has been recapitulated in a genetic model of MND, over-expressing mutant TDP-43, where TDP-43 aggregation preceded any cell death .Recent work has shown a correlation between phospho-TDP-43 and CK1 levels in sporadic MND patients and that CK1ε could act as a TDP-43 kinase in human motor neurons . Motor nThe data shown here give insight into the chronology of events in a sporadic MND model. On encountering ER stress, cells up regulate chaperone expression e.g. Grp78. Signalling downstream of cellular stress also results in activation of kinases, such as CK1, which lead to phosphorylation of TDP-43 and accumulation in the insoluble fraction. At this point, the cells do not display any metabolic deficiencies and the changes in TDP-43 modification and localization can be mitigated by small molecule CK1 inhibitors. However, at higher levels of stress, reduced ability to metabolise MTS tetrazolium is observed and at this stage, CK1 inhibitors have no effect. This underscores the importance of early diagnosis of MND as it suggests that the earliest pathological changes may be reversed by small molecules, but that this approach may be less effective once there is metabolic disturbance in the motor neurons.Fig. S1 Cellular response to endoplasmic reticulum stress. NSC-34 cells were cultured as described, differentiated for seven days and treated with 0.1 µM tunicamycin (24 h). Cells were lysed and proteins analyzed by western blotting. (a) Primary antibodies were against Grp78, TDP-43 and β-actin, with (b) densitometric analysis of normalized TDP-43 and Grp78 performed. (c) Cell viability was assessed using the CellTiter-Glo assay. Each point represents a biological replicate (n = 3) with error bars as SD. Horizontal lines (black) indicate mean and SD Statistical significance was assessed using Student’s t test with Holm-Sidak correction. Fig. S2 Validation of phospho-TDP-43 species. (a) NSC-34 cells were lysed in minimal lysis buffer and incubated with λ phosphatase, followed by SDS-PAGE and immunoblotting, probing for pTDP-43. (b) RIPA-insoluble (tunicamycin-treated) samples were separated by SDS-PAGE (± 1 mM DTT in the sample buffer), followed by immunoblotting for pTDP-43. * = high molecular weight pTDP-43 and # = monomeric pTDP-43Supplementary material 1 (PDF 1040 kb) Below is the link to the electronic supplementary material.