Laponite XLS™, which is a synthetic clay of nanometric dimensions containing a peptizing agent, has been associated with silanized hydroxypropylmethylcellulose (Si-HPMC) to form, after crosslinking, a novel composite hydrogel. Different protocols of sample preparation were used, leading to different morphologies. A key result was that the storage modulus of Si-HPMC/XLS composite hydrogel could be increased ten times when compared to that of pure Si-HPMC hydrogel using 2 wt % of Laponite. The viscoelastic properties of the composite formulations indicated that chemical and physical network structures co-existed in the Si-HPMC/XLS composite hydrogel. Images that were obtained from confocal laser scanning microscopy using labelled Laponite XLS in the composite hydrogels show two co-continuous areas: red light area and dark area. The tracking of fluorescent microspheres motions in the composite formulations revealed that the red-light area was a dense structure, whereas the dark area was rather loose without aggregated Laponite. This novel special double-network structure facilitates the composite hydrogel to be an adapted biomaterial for specific tissue engineering. Unfortunately, cytotoxicity’s assays suggested that XLS Laponites are cytotoxic at low concentration. This study validates that the hybrid interpenetrated network IPN hydrogel has a high modulus that has adapted for tissue engineering, but the cell’s internalization of Laponites has to be controlled. Hydrogels have been studied for a variety of applications these years ,2,3,4,5.Laponite is a synthetic layered silicate that can be well dispersed in water. The individual Laponite particle is in the form of disc-shaped crystals of about 25 nm in diameter and 1 nm in thickness ,15. ReceIn this study, a novel Si-HPMC/Laponite composite hydrogel has been developed. Laponite XLS, abbreviated as XLS in the following, a modified Laponite, was used to reinforce the Si-HPMC hydrogel, according to different protocols of sample preparation. The concentration of Si-HPMC was fixed at a relatively low value of 2 wt % and the XLS content ranged from 0.5 to 2 wt % for the mechanical properties test and up to 5 wt % for cytotoxicity examinations. The gelation processes and the viscoelastic properties of composite hydrogels with different XLS contents were studied by rheological methods. Confocal laser scanning microscopy (CLSM) and fluorescent microspheres were used to investigate the morphology of composite hydrogels and to track the distribution of the components. A special double-network structure was observed in this composite hydrogel. With this special double-network, the mechanical properties of composite hydrogel were significantly improved. To end this study, cytotoxicity’s assays have been performed and suggested that XLS Laponites are cytotoxic, even at low concentration.2 54.5%, MgO 26.0%, Li2O 0.8%, Na2O 5.6%, P2O5 4.1%, and the density is about 2500 kg/m3. Hydroxypropylmethylcellulose (HPMC), which was used in this study, was Methocel E4M, natural grade product with a weight-average molecular weight of about 290,000 g/mol, as determined by light scattering [w/w) was calculated as 0.59% [HPMC was first silanized by grafting GPTMS according to the method described previously . Inductias 0.59% . As Si-HPMC is only soluble in basic solutions (minimum [NaOH] = 0.1 M), 6 g of Si-HPMC powder were dissolved in 200 ml sodium hydroxide solution ([NaOH] = 0.2 M) at 25 °C with stirring for 48 h. Then, the basic Si-HPMC solution was dialyzed in dialysis bags (Spectra-por 6000–8000) against 3.8 L of NaOH solution (0.09 M) for 16 h and 4 L of NaOH solution (0.09 M) for 2 h. The final pH value of the basic Si-HPMC solution was about 12.7. An acidic buffer solution used to neutralize the basic Si-HPMC solution was prepared. Briefly, 6.2 g HEPES were dissolved in an acidic solution containing 1.8 g NaCl and 30 mL HCl (0.2 M). The volume of acidic solution was adjusted to 50 mL with deionized water and was kept stirring for about 15 min. The pH value of the final acidic buffer solution was about 3.6.The composite hydrogels were prepared according to two different protocols: The protocol A (XLS suspension in acidic buffer) is as follows: for each formulation (3), different amounts of XLS powders  were first dispersed in 10 g acidic buffer solution and the XLS suspension was adjusted to 20 g with deionized water, followed by continuous stirring for 2 h. 4 mL of basic Si-HPMC solution were blended with 2 mL of the previous acidic XLS suspension in order to obtain a composite hydrogel formulation with pH of 7.4.The protocol B  is as follows: for each formulation (3), different amount of XLS powders were first dispersed in deionized water and the mixture was stirred for 2 h to get transparent dispersions with XLS concentrations of 3, 6, and 12 wt %, respectively. Secondly, 4 mL basic Si-HPMC solution were blended with 1 mL acidic buffer solution to obtain a neutral hydrogel solution. Then, this neutral hydrogel solution was blended with 1 mL XLS dispersion and the composite hydrogel was obtained. Different concentrations of XLS lead to composite hydrogels with 0.5, 1, and 2 wt % of XLS in the both protocols. Pure unfilled Si-HPMC hydrogel was obtained when neutral hydrogel solution (basic Si-HPMC solution blended with acidic buffer before cross-linking) was added to 1 mL deionized water. If HPMC was used instead of Si-HPMC, HPMC/XLS composite hydrogels with different content of XLS could also be obtained following the same preparation protocols.As the samples undergo a rapid gelation in our experiments, the whole time used for blending different aqueous solutions was limited to 3 min. Then, the blended solutions were placed on a rotational rheometer  and the rheological properties of samples were monitored in different modes under oscillatory shear with a selected titanium cone-plate geometry . A ring with oil was used to cover the free surface of the sample in order to prevent the evaporation of solvent during measurements. The stress amplitude was fixed at 0.5 Pa, which lies in the linear viscoelastic region, and the angular frequency was 1 rad/s. The temperature was set at 37 °C during the test. A confocal laser scanning microscope  was used to investigate the dispersion of Laponite in the composite hydrogels, and Auramine O was chosen as fluorescent probe to label XLS. Briefly, XLS powders were dispersed in Auramine O solution with a concentration of 30 ppm and the dispersion was stirred for about 2 h in order to get light yellow transparent labelled-XLS dispersion. Then, the labelled-XLS dispersion was dialyzed against deionized water for about 12 h to remove free Auramine O molecules. Finally, the dialyzed labelled-XLS dispersion was used to blend with neutral hydrogel solution to form composite hydrogel, as mentioned before. A water immersion objective lens was used to observe the sample. The incident light was emitted by a laser beam at 488 nm and the fluorescence light was recorded at 510 nm. ® Polychromatic Red Microspheres 0.5 μm, Polysciences, Inc., Warrington, PA, USA) were used and the final concentration in the composite formulations were adjusted to about 5 ppm. All of the samples with fluorescent microspheres were observed under CLSM 2 h after preparation. Multi-incident light was emitted by the laser beam at 488 nm and 543 nm, and the fluorescence light was recorded at 510 nm and 560 nm. In general, images contain around 50 particles. An image size of 250 mm was considered. Trajectories of the particles were monitored for different time intervals between images. The smallest was 0.3 s. In all cases, 200 8-bit tiff images of 512 × 512 pixels were recorded and combined to make a film of the particle displacements. Motions were recorded up to 100 s. The movies were analyzed with a particle tracking routine that was developed by J. C. Cocker and E. R. Weeks. The diffusion coefficient was determined from 5 films taken from different positions in the gel.In order to observe the structure inside the composite hydrogels, fluorescent microspheres . Culture media was supplemented with 10% fetal bovine serum, 1% penicillin/streptomycin and 1% L-glutamine, and changed every 2–3 days.Human chondrosarcoma cell line  was cultured in a 5% CO2. After 24 h, the culture medium was removed and either XLS or Si-HPMC containing XLS were added onto the cell layer. For the culture in direct contact, 500 µL of culture medium containing 0.001, 0.01, 0.1, or 1% XLS (% w/v) were added to each well and refreshed every two days. For the culture in contact with hydrogel 500 μL/well of precursor solution of pure Si-HPMC or of each nanocomposite were added. The hydrogel samples were incubated at 37 °C for 1 h before adding 1 mL of culture medium. As a positive control, cells were cultured in the absence of Si-HPMC hydrogel. As a negative control, cells were cultured in the presence of actinomycin-D (5 μg mL−1), a transcription inhibitor. After 1, 2, 4, and 6 days of culture the hydrogels and culture medium were removed by aspiration. Trypan blue exclusion dye assays were performed to count the cells. In order to evaluate their mitochondrial activity, the methyl tetrazolium salt (MTS)  test was performed by adding the MTS solution to each well for 1 h. The optical density of the formazan dye was measured in a spectrophotometer at 490 nm. Each condition was tested in quadruplicate.Cell viability in two-dimensions (2D): two different experiments have been performed, (1) evaluation of SW1353 cell viability cultured in direct contact with increasing concentrations of XLS and (2) evaluation of SW1353 cell viability cultured in contact with Si-HPMC hydrogel containing increasing concentrations of XLS. SW1353 cells were allowed to attach to 24-well plates at a final density of 10,000 cells per cm−1) was used as an internal control for cell death. In each well, the culture medium was replaced by 200 μl of a solution containing 2.5 mL of DMEM medium that was supplemented with 0.25 μL of calcein-AM and 5 μL of ethidium homodimer-1. After 45 min, the dye mixture was removed and the hydrogels were intensively rinsed with phosphate buffered saline solution before being observed with a confocal laser scanning microscope ), equipped with an argon/krypton laser. Each condition was tested in quadruplicate and for each sample two random positions  were chosen within the hydrogel and a series of images were recorded starting from these positions along the z-axis. The 210 images that were obtained per sample were analyzed with a quantimeter Q550 .Cell viability in three-dimensions (3D): 3D culture cell viability was assessed by using Live&Dead Assay Kits  along with confocal image analysis. SW1353 cells were dispersed in the hydrogel precursor solutions within 5 min, following their preparation (thus before gelification) at a final concentration of 1,000,000 cells per ml of hydrogel. Five hundred microliters of each mixture were molded into 24-wells plates and incubated at 37 °C for 1 h to allow for the gelification of the hydrogels. Afterwards, 1 ml of DMEM medium was added per well and the samples were incubated for 1, 2, and 6 days before the Live&Dead assays. Actinomycin-D treatment (5 μg mLw/v) of Laponites. After incubation, cells were rinsed with PBS and were detached with a trypsin solution. After gentle centrifugation, the cells were fixed several days at 4 °C with a 1.6% of glutaraldehyde in 0.1 M cacodylate buffer solution (pH 7.4), then rinsed several times with a 0.1 M cacodylate buffer solution (pH 7.4), and post-fixated during 45 min at room temperature with a 1% osmium tetroxide in 0.1 M cacodylate buffer solution. After post-fixation, cells were rinsed several times with water and were dehydrated in gradually concentrated baths of ethanol and then of propylene oxide before being impregnated and embedded in EPON resin. Blocks containing cells were cut in thin layers (<100 nm) by ultramicrotomy  with a diamond knife and deposited on copper grids that were previously covered with a thin collodion film. Grids were then contrasted with lead citrate and uranyl acetate solutions before being observed. The TEM observations were performed in the conventional bright field mode with a transmission electron microscope , operating at 80kV and equipped with a four Megapixels retractable digital CCD camera .For TEM experiments with cells and Laponites, adherent chondrosarcoma cells (SW1353) were first grown until confluence in multiwell culture plates before being incubated during 24 h or 48 h with DMEM media containing 1% .The results are expressed as mean ± SEM. Statistical analysis was carried out using two-way ANOVA. Where significant overall differences were detected by ANOVA, Fisher’s test was used for further difference comparison as sample sizes are small. HPMC or Si-HPMC is white powder in appearance and it only dissolves in basic solution, as mentioned before. When the basic Si-HPMC solution is blended with acid buffer solution, the mixture is neutralized and the pH value decreases to about 7.4, which causes sodium silanolate transformation into silanol and leads to a condensation reaction . Typical evolution of the storage modulus during one day are shown in On the basis of the nature of their crosslinks, hydrogels can be classified as chemical hydrogels when the crosslinks are covalent bonds, or as physical hydrogels when the links are secondary weak bonds, such as Van der Waals, electrostatic interactions, hydrogen bonds, or molecular entanglements. It is possible to distinguish them from mechanical spectra. 1 meaning that the HPMC is in a viscous solution. For HPMC/XLS formulation  was used to investigate the dispersion of Laponite in the composite hydrogels.D is diffusion coefficient. D is related to viscosity η of the surrounding matrix through the radius r of the moving particle (in our case fluorescent microspheres) [R is gas coefficient, T is absolute temperature, and aN is Avogadro’s constant. Using Equations (1) and (2), the local viscosity of matrix surrounding the moving particles can be calculated from displacements that were recorded by CLSM. As particles can make Brownian motion in fluids and this random movement can be recorded and described by mathematical models, fluorescent microspheres have been added in the composite hydrogels and the movements of these fluorescent microspheres have been recorded by CLSM. The root mean square displacement of microspheres moving in any direction after time t can be expressed, as following :(1)x¯=2Dspheres) :(2)D=RT6The Brownian motions of fluorescent microspheres were visible in physiological saline and in XLS suspension in deionized water, and the viscosities that were calculated from microspheres movements in these two formulations were about 0.001 Pa.s, identical to that of water. When the concentration of NaCl increased to the level of physiological saline, in which the concentration of NaCl is about 0.91 wt %, Laponite particles suspended in the aqueous solution began to aggregate and the viscosity of the system increased to about 0.003 Pa.s. Even in this high ionic strength, aggregated mass of Laponite in pure saline solution was still faint under CLSM, as shown in Following the Physico-chemical investigations, we evaluated the cytotoxicity of our constructs (i) in 2D culture with the XLS suspension placed on the top of the cell layer, (ii) in 2D culture with the hydrogels that are placed above the cell layer, and (iii) in 3D culture with the cells embedded within the hydrogels.As shown in The MTS activity was also monitored in 2D with the cell layer in contact with the composite hydrogels (Si-HPMC/XLS). The results, which are plotted in These results showed an influence of the protocol used for the formulation that contain 1% XLS Laponites and above. A global significant decrease of the MTS activity was observed at all time points with protocol A as compared to B. From day 1 to 3, for all of the formulations with 0.2% to 2% of Laponites, MTS activity increased. At day 1, MTS activity was significantly lower at 0.2 and 0.5% XLS prepared with protocol A as compared to B. Then, the MTS activity significantly increased with protocol A at 1 and 2% of XLS, and then significantly decreased at 3 to 5% XLS. When prepared with B, MTS activity was not significantly different between 0.2 to 2% XLS, but decreased significantly for concentrations of XLS above 2%. At 3% of Laponites and above, MTS activity was significantly decreased when compared to control cells, regardless of the time of culture and was comparable to the residual MTS activity measured when the cells are treated with Act.D. Following the 2D cytotoxicity assays evaluation, 3D cell proliferation was investigated . The conTo understand the toxic effect of Laponites nanoparticles on the SW1553 2D cell in 2D culture, Laponites (at 1 wt %) were removed after 1 or 2 days, and cells were analyzed under Transmission Electron Microscopy. Without Laponites, trypsinized cells showed round shape with distinct organelles and nucleus The first part and aim of this study was to follow the influence of Laponite concentration on the structure of the formulations using two different preparation protocols. Rheological measurements were used, and it was observed that in the presence of HPMC or Si-HPMC a gel was formed. With HPMC a quite rapid increase of the modulus was seen before the first data point measurement and the evolution of the modulus was quite weak at longer times. On the contrary, the storage moduli of Si-HPMC and Si-HPMC/XLS formulations increased relatively gradually, but reached a higher value as compared to those of the HPMC/XLS formulations. These phenomena suggested that the formed network structures in HPMC formulation and Si-HPMC formulations might be different.The modulus of HPMC/XLS system with 2 wt % XLS prepared using B without any covalent network structure could also reach a high value (about 780 Pa), which further indicated that some special structure might formed in hydrogel system that was prepared using B.The mechanical spectra suggest that there is only a weak physical network structure in HPMC/XLS composite hydrogel system, while chemical network structures were formed in the Si-HPMC pure or composite hydrogels prepared using B.As HPMC is an inert cellulose derivative without any reactive group on the polymer backbone, the pure HPMC solution showed a typical viscous flow behavior, as expected. When HPMC was silanized, silanol groups that were grafted onto the polymer chain could react with each other on decreasing pH, causing condensation reaction and forming chemical network structure . When XLWhen Laponite particles are mixed with Si-HPMC, we can observe on The reason why the different protocols lead to the different structures in composite hydrogels can be explained, as follows. In protocol A, Laponite particles firstly meet high ionic strength buffer solution, which induces the immediate aggregation of particles and the formation of compact clusters. When polymer is later introduced, it is hard for the long polymer chains to penetrate into the clusters. Therefore, the polymer only adheres outside. The picture is simply that of essentially Laponite clusters that are bridged by Si-HPMC network. However, in B, Laponite particles meet polymer and electrolyte simultaneously. Then, the long polymer chains can connect with Laponite particles and react before they aggregate. When long polymer chains are added, the aggregation of Laponite in the hydrogel system that is prepared using B is totally different from that prepared using protocol A. There are two parts in this system: a dense Laponite rich part and Laponite free or loose part. As silanols on Laponite can react with silanol on cellulose, the long polymer chains can, not only adsorb onto Laponite particles, but also covalently bridge particles. In solution with high ionic strength, Laponite tend to aggregate. Then, the Laponite aggregates and linked polymer chains built a special compact network in the composite system. Even if there are many Laponite particles that are connected with long polymer chains, it is likely that the shape of the dense structure can be affected by the flow of polymer solution during the blending process. This could be the reason why the aggregated mass is irregular under CLSM. Meanwhile, the area outside the compact part is only polymer network and it is relatively loose. The compact network and loose native chemical network form co-continuous structures and the modulus of the sample is dominated by the modulus of the Laponite dense structure, leading to the high modulus of the composite hydrogel. The loose chemical network in the dark area is not changed with the addition of XLS, being similar with that of pure Si-HPMC hydrogel, which provides enough continuous space for the nutrient transportation and cell cultivation. Generally, scaffolds for tissue engineering should be highly porous, have adequate mechanical stability and strength, and contain interconnected pores to permit cell penetration and in-growth into the implanted structures ,33. ThesPrevious studies have demonstrated that internalization of Laponite nanoparticles induces cell death ,35. OtheHere, we present evidence that free XLS Laponites  at conceIn 2D with hydrogel above cell layer , at XLS Using protocol B, in 3D , we obseOverall, our data demonstrated that a fine tuning of the kinetics of hydrogel/XLS composite gelation will be needed to minimize toxic effects XLS Laponites and cell internalization.This study validates the IPN hybrid high modulus hydrogel for tissue engineering and animal model will then be used for the biocompatibility investigations of these new IPN hybrids.A novel silanized hydroxypropylmethylcellulose/laponite composite hydrogel with great improvements in modulus has been prepared. Rheological experiments disclosed that there were both chemical and physical network structures in the composite hydrogel. Morphologies of Laponite dispersion were observed by CLSM directly and showed that different composite structures can be formed following different protocols. The Brownian motions of fluorescent microspheres in the systems revealed that there are two areas in the Si-HPMC/XLS composite hydrogel: compact areas, composed of aggregated Laponite and entangled polymer chains, both connected through covalent bonds and loose areas with a diluted crosslinked polymer network. This special double-network structure made this composite hydrogel system have great application potential in tissue engineering as biomaterials: The compact continuous network leads to the high modulus of the composite hydrogel, and the loose area provides continuous room for the nutrient transportation and cell cultivation. The biological investigations have clearly demonstrated toxicity of free XLS laponites. However, once the composite hydrogel is set, low toxicity is observed and the cells could resume their proliferation. The tuning of the kinetics of the setting process seems to be the critical parameter for the further use of these composite hydrogels for tissue engineering applications well adapted to be tested in long term for biocompatibility and biofunctionalities in different animal models for different applications.


Model results showed that hyperthermophilic methanogens dominate in systems where a narrow flow path geometry is maintained, while thermophilic methanogens dominate in systems where the flow geometry expands. At Axial Seamount, the residence time of fluid below the surface was 29–33 h. Only 1011 methanogenic cells occupying 1.8–18 m3 of ocean crust per m2 of vent seafloor area were needed to produce the observed CH4 anomalies. We show that variations in local geology at diffuse vents can create fluid flow paths that are stable over space and time, harboring persistent and distinct microbial communities.The size and biogeochemical impact of the subseafloor biosphere in oceanic crust remain largely unknown due to sampling limitations. We used reactive transport modeling to estimate the size of the subseafloor methanogen population, volume of crust occupied, fluid residence time, and nature of the subsurface mixing zone for two low-temperature hydrothermal vents at Axial Seamount. Monod CH Epsilonbacteraeota living in the global ocean crust at deep-sea hydrothermal vents contain an estimated 1.4–2.7 Gg C and produce 0.045–1.4 Tg of organic C yr−1, suggesting that microbes in the shallow hydrothermal subseafloor are a relatively small standing stock which turns over rapidly  (μmol kg−1), concentration of a thermophilic methanogen with M. thermolithotrophicus growth kinetics, [Mthe] (cells L−1), concentration of a hyperthermophilic methanogen with M. jannaschii growth kinetics, [Mjan] (cells L−1), concentration of Mg2+, [Mg2+] (mmol kg−1), and temperature, T (K). Temperature was calculated assuming pure mixing and was given by the following equation,The model state variables were concentration of CHfht is the fraction of high-temperature fluid in the box determine from the Mg2+ content of the venting fluid assuming 0 mmol Mg2+ kg−1 in the hydrothermal end-member, Tht is the temperature of the hydrothermal end-member fluid, Tsw is the temperature of seawater, and Cp,ht and Cp,sw are the heat capacities of hydrothermal end-member fluid and seawater, respectively. During hydrothermal circulation, Mg2+ is removed from solution within hours at high temperatures and the Mg2+ content of diffuse fluids indicates how much hot, zero-Mg2+ end-member is in the fluid [where he fluid .i, which were solved using the method-of-lines. The model was implemented in the R software environment using the package ReacTran [The rest of the variables were described by the following system of differential equations for each box ReacTran .3\documeQ is fluid flux. Methane production (RCH4) is calculated by:where vmax is the maximum rate of cell-specific CH4 production, KH2 is the half-saturation constant for cell-specific CH4 production, and Tmax is the optimum growth temperature of the methanogen. A conversion factor of 10−9 is used to convert νmax, which in Table −1 h−1 to μmol cell−1 h−1.where Growth rate for methanogens is given by,A is the Arrhenius constant and Ea is the activation energy.where Methanocaldococcus species, Methanothermococcus species, CH4, and H2 concentrations in exiting fluids at individual diffuse vents were used to constrain the modeled subseafloor methanogen abundance, CH4 production, and the shape function of fluid mixing for each vent. Dissolved inorganic carbon (DIC) was also measured in the fluids to ensure that it was not growth limiting. Thirty-seven diffuse hydrothermal fluid samples were collected from Marker 113 and Marker 33 . Temperature and chlorinity for the high-temperature end members were determined from the relationship of Mg2+ and temperature (or chlorinity) and extrapolated to zero Mg2+ concentration. The nearest high-temperature vents with similar Cl− end-members are Diva vent  and El Guapo vent (end-member Cl− 400 mmol kg−1 and end-member H2 120–470 µmol kg−1). End-member H2 concentrations at Marker 113 and Marker 33 were assigned values of 950 and 300 μmol kg−1, respectively. These end-member H2 values for the diffuse vent sites reflect the fact that Marker 113 has a vapor-dominated source with higher gas content than the source for Marker 33.There was no high-temperature hydrothermal venting within 0.5 km of Marker 113 or Marker 33 that could provide hydrothermal end-member gas concentrations. Therefore, high-temperature end-member HM. jannaschii was grown in 11 separate chemostat runs and M. thermolithotrophicus in 9 separate chemostat runs at varying H2 concentrations, dilution rates, and temperatures  was 43 ± 5 fmol CH4 cell−1 h−1 (± standard error) and the KS for H2 was 37 ± 15 µM. For M. thermolithotrophicus, the Vmax was 24 ± 3 fmol CH4 cell−1 h−1 and the KS for H2 was 27 ± 12 µM. The growth rates of both organisms in a closed system  were measured over their growth temperature range to determine their Arrhenius constants  was 86.3 kJ mol−1 and the pre-exponential factor (A) was 9.16 × 1012 h−1. For M. thermolithotrophicus, the Ea was 73.8 kJ mol−1 and A was 3.36 × 1011 h−1  and 2.9 ± 0.9 × 107 cells L−1, respectively. In contrast, at Marker 33, only 2–5% of annotated metagenomic sequences were assigned to known methanogenic genera, primarily hyperthermophilic Methanocaldococcus species  and shape parameter (xb) were adjusted until the H2 and CH4 concentrations and ratio of thermophiles to hyperthermophiles fit those observed in the vent fluid outflow  across varying H2 concentrations and temperatures. In this study, M. jannaschii and M. thermolithotrophicus were grown in the chemostat at maximum cell concentrations such that all the available H2 in the reactor was consumed, which closely resembles the complete depletion of H2 in diffuse vent fluids exiting the seafloor. However, when M. jannaschii was grown in a chemostat at 80–83 μM H2 and 10-fold lower cell concentrations such that excess H2 was observed in the headspace, the growth yield (Yp/CH4) dropped ~10-fold and the cell-specific CH4 production rate increased to ~ 500 fmol CH4 cell−1 h−1 [Methanothermobacter thermoautotrophicus and the mesophile Methanococcus maripaludis when H2 was limiting [2 availability and low hyperthermophilic methanogen growth yields at hyperthermophilic temperatures that lead to much higher CH4 production rates per cell. In this study, the cell-specific CH4 production rates are conservative and the estimated number of total methanogens at each Axial vent in this study should be considered upper limits. This means that the population of methanogen cells necessary to support substantial methane anomalies could be even smaller than we have predicted.Another possible caveat for our model is that it assumes a constant growth yield (i.e., amount of cell mass produced per mol of CHll−1 h−1 . Growth limiting –41. Ther3 of subsurface crust and consisting of 0.4–1.3 × 1011 total methanogen cells m−2 of vent area. The model showed that the differences in the methanogen populations at Marker 113 and Marker 33 can be explained by differences in the geometry of the subsurface hydrology. Therefore, small-scale variation in the geologic fabric of the upper crust can create varied fluid flow paths fixed in space that harbor persistent and distinct microbial communities, whose metabolic activity produces microbial and chemical signatures at seafloor vents.This study used two representative methanogens as tracers of subsurface fluid circulation and microbial production at two diffuse vents at Axial Seamount. By combining laboratory-derived methanogen growth kinetics with reactive transport modeling and in situ observations, the population size of methanogens, the volume of crust occupied by these organisms, the fluid residence time, and the nature of subsurface mixing were estimated. Results suggest that the methanogen population at each vent was relatively small and local, occupying as little as 2 mSupplemental Material


Alternatively, nonradioactive northern blot relies on chemiluminescent reactions triggered by horseradish peroxidase (HRP) conjugated probes. The use of regulated radioactive material and the complexity of chemiluminescent reactions and detection have hampered the adoption of northern blot techniques by the wider biomedical research community. Here, we describe a sensitive and straightforward nonradioactive northern blot method, which utilizes near-infrared (IR) fluorescent dye-labeled probes (irNorthern). We found that irNorthern has a detection limit of ∼0.05 femtomoles (fmol), which is slightly less sensitive than 32P-Northern. However, we found that the IR dye-labeled probe maintains the sensitivity after multiple usages as well as long-term storage. We also present alternative irNorthern methods using a biotinylated DNA probe, a DNA probe labeled by terminal transferase, or an RNA probe labeled during in vitro transcription. Furthermore, utilization of different IR dyes allows multiplex detection of different RNA species. Therefore, irNorthern represents a more convenient and versatile tool for RNA detection compared to traditional northern blot analysis.Northern blot analysis detects RNA molecules immobilized on nylon membranes through hybridization with radioactive  Unlike methods such as reverse transcription PCR (RT-PCR) and RNase protection assay, northern blot analysis has the ability to distinguish RNA molecules based on size . This fe32P  dyes, which have emission wavelengths in the spectrum of 650–900 nm, has become increasingly important in the detection of biomolecules . Low aut3)-modified nucleotide in the DNA probe, copper-free click chemistry can be used to covalently link the azide with the Dibenzocyclooctyl (DBCO) IR dye. This allows for highly efficient and stable incorporation of the IR dye into the probe DNA probe with an IR dye using a protocol modified from Trichoplax adhaerens (Ta) U6 snRNAs. Based on the analyses of five independent experiments, IR dye-labeled probes are capable of detecting RNA as low as 0.02–0.05 fmol . This sensitivity is comparable to that of the nonradioactive “locked nucleic acids (LNA), 1-ethyl-3-(3-dimethylaminopropyl), and digoxigenin” (LED) method which has been shown to detect 0.05 fmol of small RNAs . Therefore, irNorthern is slightly less sensitive compared to 32P-Northern.The sensitivity of IR dye-labeled DNA probes in a northern blot was measured by probing a series of in vitro transcribed all RNAs . For com32P-labeled probes, whose half-life is ∼14 d . To illustrate this advantage for irNorthern, we probed for U6 snRNA on a northern blot with intervening lanes of nuclear and cytoplasmic RNAs from human embryonic kidney cells  A. By adj32P] NTP and can have higher specificity due to their long lengths  hairpins processed by Dicer . Furthermore, IR dye-labeled probes can be stored and reused for extended periods of time   , which iWhile our multiplex irNorthern displayed its functionality in pre-miRNA processing, this method also has applications in other RNA processing reactions such as mRNA splicing , in whicPreviously described improvements on the northern blot method could be implemented with the use of IR dye-labeled probes to produce even lower detection limits. For example, replacing UV crosslinking with 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) crosslinking results in a 50-fold increase in small RNA detection . Further32P labeled probes, even though we were able to produce DNA and RNA probes labeled with hundreds of IR dyes (data not shown). However, improving IR technologies that minimize the self-quenching effect could allow for more sensitive irNorthern probes to be synthesized with ease in the future . To label the azide-modified oligonucleotides with IR dye, 2.5 nmol oligonucleotides were mixed with 50 nmol IRDye 680RD or 800CW DBCO (Li-Cor Biosciences) in phosphate buffer saline , 100 pmol U6 DNA probe (5′-GCAGGGGCCATGCTAATCTTCTCTGTATCG-3′), and 100 to 1500 pmol of 5-azidomethyl-dUTP (Jena Bioscience). After incubation at 37°C for 1 h, the reaction was purified by Ampure XP beads, labeled with IRDye 800CW DBCO, and purified again by Ampure XP beads as described above. Labeled probes were separated on an 8 M Urea 15% acrylamide gel. After IR scan and SYBR green imaging 3, 8 mM MgCl2, 1 mM ATP, 1 mM UTP, 1 mM GTP, 1 mM CTP, 2 U/µL Murine RNase Inhibitor (NEB), 10 mM DTT, and 0.4 U/µL T7 RNA polymerase was incubated at 37°C overnight. In the transcription reactions including Aminoallyl-UTP-ATTO-680, 1 mM UTP was replaced by 50 µM UTP and 50 µM Aminoallyl-UTP-ATTO-680 (Jena Bioscience). In the reactions including 5-Azido-C3-UTP, 1 mM UTP was replaced by 1 mM total UTP with different ratios of UTP:5-Azido-C3-UTP (Jena Bioscience) as indicated in 2O. The 5-Azido-C3-U containing RNA probes were labeled with IRDye 800CW DBCO and purified by Ampure XP beads using the conditions described above.To synthesize antisense RNA probes for 32P-ATP (PerkinElmer), 10 U T4 PNK (NEB) in 70 mM Tris-HCl, 10 mM MgCl2, 5 mM DTT, pH 7.6 at 37°C for 30 min. The 32P-labeled oligonucleotides were then purified by a Microspin G-25 column according to the manufacturer's instructions.Ten picomoles of U6 DNA probe was incubated with 150 µCi 6000 Ci/mmol γ-2 using a Stratalinker UV crosslinker 2400 (Agilent genomics). After prehybridization with 10 mL ExpressHyb Hybridization Solution (Clonetech) for 30 min at 37°C, the membrane was hybridized with 10 pmol IR dye-labeled oligonucleotide U6 probe in 10 mL ExpressHyb solution at 37°C overnight. The membrane was washed with 2× SSC, 0.1% SDS (10 min at room temperature), 1× SSC, 0.1% SDS (10 min at room temperature) and analyzed by a Li-Cor Odyssey CLX scanner. The membranes used for irNorthern in In vitro transcribed Ta U6 RNAs were electrophoresed in an 8 M Urea 10% acrylamide gel and then transferred to a Hybond N+ membrane (Amersham). The membrane was cross-linked twice with 254 nm UV light at 120 mJ/cm32P-labeled DNA probe, the protocol was the same as irNorthern, except that the washed membrane was exposed to a phosphor screen overnight and scanned on a Storm phosphorimager (Amersham). In between probing with different DNA probes, the membranes were stripped with microwave-boiled 0.1× SSC, 1% SDS, 40 mM Tris-HCl pH 8.0 for 10 min twice. The membranes were rescanned to ensure the probes were successfully removed. When comparing the sensitivity of IR dye-labeled probes to 32P labeled probes, we alternated the first probe applied to the membrane in five different experiments and found that probing the membrane first with either the IR dye-labeled or the 32P-labeled probe did not affect our conclusions .For northern blots using a For irNorthern using biotinylated DNA probes, after overnight hybridization and brief washes of 2× SSC, 0.1% SDS (2 min at room temperature), and 1× SSC, 0.1% SDS (2 min at room temperature) the membranes were incubated with 1:10,000 diluted IRDye 800CW streptavidin in ExpressHyb solution at 30°C for 30 min and then washed and analyzed as described above.For irNorthern using an RNA probe, the hybridization was carried out at 65°C overnight. The membranes were washed with 2× SSC, 0.1% SDS (15 min at room temperature), 1× SSC, 0.1% SDS (15 min at room temperature) and 0.2× SSC, 0.1% SDS (15 min at room temperature) and analyzed as described above. Unlike the DNA probes, RNA probes cannot be completely stripped off the membrane.As probes were continuously reused during experimentation, all IR dye-labeled probes diluted in hybridization solution were stored at −20°C while stock solutions of IR labeled probes were stored at −80°C. IR dyes and IR dye-labeled probes were protected from strong light exposure during irNorthern procedure and storage.Supplemental material is available for this article.


The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions. Methods are being sought that allow early and accurate diagnosis of cancer, and to precisely differentiate the character of the lesion and qualify for biopsy. X-ray mammography has reduced breast cancer mortality up to 45%, and is recommended as a screening method2, but its sensitivity is limited in women with dense breasts. In a group of patients with extremely dense breasts it is reported to be 48%3. Conventional ultrasound (US) is a complementary examination in women with dense breasts. It allows assessment of the cancer features according to the BI-RADS lexicon4, being faster, cheaper, and more accurate than mammography6. On the other hand, US has limitations such as reproducibility and quality of the equipment, and therefore is operator and device-dependent8. It is worth noting the great development of machine learning techniques in the field of breast cancer classification. Methods based on B-mode image analysis can achieve very high diagnostic performance13. A systematic review of the efficacy of cancer classification using various methods of machine learning and imaging diagnostics, including ultrasound, can be found in Yassin et al.14.Breast cancer is still one of the leading causes of cancer-related death for women worldwide15. The combination of QUS methods and the BI-RADS assessment has the potential to better identify patients with benign lesions and at the same time increases the accuracy of breast lesion classification16. The use of quantitative parameters enables clinicians to limit the number of biopsies, while still maintaining the same level of detection in terms of malignant cases17. Tissue traits that serve as biomarkers of neoplastic change are usually related to tissue scattering properties, the statistical properties of echo or the texture of tumor images and parametric maps. This classification is based on the differences in the values of these parameters between neoplastic and benign lesions that reflect changes in tissue structure caused by tumour development.Evaluation of tumors based on the BI-RADS classification can be improved by including quantitative ultrasound (QUS) methods. While the B-mode ultrasound does not provide reliable quantitative information on the tissues examined, QUS techniques allow for the evaluation of tissue in terms of its structure and mechanical properties. They can be a valuable complement to ratings based on BI-RADS. Some QUS parameters are closely related to BI-RADS descriptors, while others go beyond the tumor traits described there18. It is important for classification techniques that the specific morphological characteristics of cancer tissue are associated with malignancy and have a significant impact on the tumour image and parameters calculated from its ultrasonic echoes. These features result in more complex scatterer composition in malignant tumours19, which can lead to specific signature of backscattered echoes, in QUS.Histopathological analysis shows the differences between the tissue of benign and malignant tumours. Malignant tumours are rich in cellularity; the cells tend to form cell clusters. Benign tumours have more regular arrangement of the cells. Additionally, invasive ductal carcinoma, which forms the majority (80–90%) of invasive breast cancers is characterized by a significantly increased amount of dense fibrous tissue stroma20. One of the most important distributions used for modelling soft tissue scattering is the Nakagami distribution21. A good description of tissue scattering statistics and its ease in determining the value of the shape parameter made the distribution of Nakagami very popular in QUS techniques27. Another feature studied in the context of tumour classification is the information entropy, which is a measure of uncertainty of a random variable29. Hughes proposed the use of entropy in quantitative assessment of changes in scattering medium structure30. Tsui et al. have shown that entropy is sensitive to the variability in concentration of scatterers in tissue phantoms31. They then proposed a parametric imaging method based on the entropy determined in small windows32. Another example of the QUS parameter is the texture of the ultrasound image, which also reflects the variability in tumour’s structural echogenicity, as it describes the correlation between spatial distribution and the intensity of pixels in the image. The most often used texture-feature parameters are extracted from the gray-level co-occurrence matrix (GLCM). This method was first described by Haralick et al.33 and now is often used in ultrasound tumour classification34.The scattering properties of tissues can be evaluated by modelling the statistics of the signal envelope using the probability density function (PDF). An overview of distributions used to model scattered signals in soft tissue can be found in the work of Destrempes and Cloutier18. They differ in morphological features of the cells and essentially the stromal component proliferation. Malignant tumours are not encapsulated, not cohesive and characterized with irregular pattern of growth. Their borders are not well defined and they spread into adjacent tissue rather than displacing or pushing it aside and cause its damage. On the contrary, benign tumours usually have a covering made up of normal cells and their borders are mostly well-defined. They do not penetrate the adjacent tissue and do not damage it.The results of the breast change classification presented so far have been based on data from the inside of the assessed tumours. However, there are reasons to also use data from the tissue surrounding the tumour, as malignant and benign lesions can have different effects on neighboring tissueset al.35 studied the imaging of tumours in 121 patients using a mammography, sonography and magnetic resonance methods to assess which one is the most accurate in pretherapeutic sizing of primary breast cancer. Tumour size was found to be significantly underestimated in ultrasound, and the mean difference between the sonographic and histological size was 8 mm. It is worth noting that the greatest difference between sonographic sizing and actual histological tumour size was found with invasive lobular breast cancer. These results have been confirmed by Stein et al. who analysed data from 6543 breast cancer patients and assessed the accuracy of tumour size measurement by ultrasound36. The mean tumour diameter determined by ultrasound was 18.3 mm, whereas the histological mean tumour diameter was 20.8 mm. From these results it follows that the analysis of data limited only to the area of the tumour visible in the B-mode image does not take into account tissue changes in the peripheral area of the tumour and its close vicinity. These areas are important in the classification due to their various modifications during the development of malignant and benign tumours.Furthermore, the B-mode examination underestimates the size of the tumour. Gruber The presented study investigated the possibility of distinguishing between malignant and benign tumors using QUS parameters calculated on the basis of signals scattered in the tissue surrounding the tumors, which is the main novelty of the research. The ultrasound data was acquired from 116 patients diagnosed with a suspicious breast lesion. The parameters used in tumour classification were the shape parameter of the Nakagami distribution, the entropy, and ten texture parameters. In our study, parameters determined for the tumour and the rim of the tissue surrounding the tumour were used as independent features in the construction of multi-parametric classifiers.The observation in this study suggests that the QUS examination of the peritumoral tissue is more effective than the study of the tumour tissue itself. A multi-parametric classifier operating on data from tissue surrounding tumours distinguished changes better than the best classifier operating only on cancer data. On the other hand, the classifier consisting of parameters based separately on tumour and peritumoral tissue data proved to be the best, which suggests the complementarity of quantitative ultrasound information contained in the tumour and external tumour tissue.® machine  equipped with a L14-5/38 linear probe. The transmit frequency was set at 10 MHz (centre frequency ~7.2 MHz), and the sampling frequency was equal to 40 MHz. The focus was set at the middle of tumour and the number of lines per image was 510. The breast US examinations were performed according to the American College of Radiology BI-RADS guidelines using longitudinal and transverse scan planes4. Lesions of solid BI-RADS category 3, 4, or 5 were included in this study. Ultrasound data from 116 tumours were collected, including 57 malignant and 59 benign cases. The group of malignant lesions contained following tumor types: ductal (21), lobular (10), cribriform (8), tubular (5), micropapillary (3), and mixed (10). The tumors were assessed as low grade (13), intermediate grade (35) and high grade (9). The tumour contour was determined manually by an experienced breast sonographer. Minimal, mean and maximal tumour equivalent diameter  in the data set was equal 4.7, 12.7, and 26.6 mm respectively. Core biopsy samples were taken from patients with BI-RADS 4 and 5 lesions. Patients with BI-RADS 3 underwent the fine needle aspiration biopsy (FNAB) and 2 year follow-up. Ultrasound data were categorized on the basis of histopathological, cytological and patient observation findings that diagnosed benign or malignant tumour.Research was carried out in the Department of Radiology, Maria Skłodowska-Curie Memorial Institute of Oncology in Warsaw. The Institutional Review Board approved the study protocol. All the procedures performed in the study that involved human participants were in accordance with guidelines set by the 1964 WMA Declaration of Helsinki and its later amendments or comparable ethical standards. All patients signed the informed consent for breast US examination and for ‘backscatter US’ statistical studies. Ultrasound B-mode images and radiofrequency data (RF) were acquired by an experienced sonographer using an Ultrasonix SonicTOUCHQUS analysis was performed on selected regions of interest (ROI) – a tumour area  and an area surrounding the tumour . An example of a tumour image with the ROIs as well as the further data processing scheme is shown in Fig. 21:NAK is the shape parameter, and NAK was estimated using the method of moments21 according to the following formula  of the Nakagami distribution is given by equation  21:1\doc37 was used as a measure of local signal envelope heterogeneity. Weighted entropy (ENT) was estimated using formula . Based on each GLCM a number of texture parameters were calculated. These were: contrast (CON), correlation (COR), energy (ENE), homogeneity (HOM), and variance (VAR):i and j indicated the discrete gray levels and were also the indices of GLCM elements. The μ and σ denoted the mean and standard deviation of the i and j coordinates. It must be noted that each GLCM was normalized  before determination of said parameters.The textural features of the US images were extracted using the Gray Level Co-occurrence Matrix (GLCM). The GLCM is a matrix that contains probabilities of occurrence of certain gray tones in a pair of pixels being in a particular relative spatial position. For the purpose of the GLCM calculation the gray scale of the ultrasound images was limited to a 20–100 dB range and quantized into 20 gray levels which resulted in GLCMs of size 20 × 20. The spatial relationship in any considered pair of pixels was defined as a vertical or horizontal displacement by 0.3 mm (4 pixels). We considered the vertical and horizontal textural features to carry different information, and therefore the GLCMs obtained for vertical and horizontal spatial relationships were used for determination of separate texture parameters marked with the letters ‘V’ and ‘H’ respectively. This approach is different in comparison to the previously cited worksmm × 1 mm, which corresponded to three times the pulse length, as recommended by Tsui et al.38. Window overlap in the axial and lateral direction equaled 92%. Parametric maps were corrected to minimize the impact of acoustic beam formation and the system transfer function. Data from the reference tissue phantom  was obtained with the same scan settings as in the patient study. This data was used to generate reference parametric maps. Based on these maps, correction curves were determined for each of the considered parameters, which were then used to improve the parametric maps of tumors and their surroundings. Next, for each tumour the parameter value was averaged over all pixels of parametric maps in the two scanned planes of tumour. The averaged parameter was used as a predictor of neoplastic changes in subsequent classifications. The data processing scheme is presented in Fig. The spatial parametric images of tumours and the surrounding rims were generated using the sliding window technique. The parametric image represented a map of the parameter value distribution in the analysed ROI, external or internal. The parameter value  was estimated based on the block of ultrasonic data from each window. The window size was equal 1 39 was used, with k equal to 4 and standardized Euclidean distance. Classifiers were cross-validated through the ‘leave-one-out’ technique40. Evaluation of the classification results was based on the analysis of the receiver operating characteristic (ROC) curves41, in particular the area under the ROC curve (AUC), sensitivity, specificity, and accuracy. In order to compare the effectiveness of the classification based on internal and external ROI data, AUC values were determined for all single-parameter classifiers (SPCs) and multi-parameter classifiers (MPCs) built up of all the possible combinations of single parameters. This ‘exhaustive search’ approach42 was applied to three types of MPCs. The first one consisted of parameters determined using data from the tumour , the second from data collected from the tumour rim , and the third one used both types of parameters. The best classifiers of each type were chosen based on their highest AUC values. Parameters included in selected MPCs are shown in the results section. To assess the statistical significance of classification results, corresponding p-values were determined through a two-sided Wilcoxon rank sum test. All calculations were done using Matlab® 2017a .Twelve parameters were considered including Nakagami parameter, weighted entropy, and ten texture parameters determined from the GLCM matrix, five for each vertical and horizontal spatial relationship. For the classification of tumours based on single-parameter and multi-parameter classifiers, the k-nearest neighbours (k-NN) algorithmBoxplots characterizing individual parameters values estimated from benign and malignant tumours are presented in Fig. NAK, CONH, ENEV, HOMV, and VARH were components of the best internal classifier. Parameters CORV, CORH, HOMV, VARV, and VARH were components of the best external classifier. The combined multi-parametric classifier used the parameters CORV and ENEV determined from the tumour and CONV, ENEV, ENEH, HOMV, and HOMH determined on the basis of data from the tissue surrounding the tumour.The AUC, sensitivity, specificity, accuracy, and statistical significance of SPCs and the best  MPCs are presented in the Tables entclass1pt{minimaCONH and CORH. The greater difference between medians generally translates into a higher classification efficiency. This is clearly visible in Fig. CONH and CORH parameters. The largest difference in AUC was observed for the HOMV parameter (ΔAUC = 0.22). The AUC values for external SPCs in six cases reached values above 0.8. P-values for SPCs indicated that most of them are statistically significant from the point of view of differentiation of benign and malignant tumours. Among external SPCs, 11 out of 12 were characterized by a very high statistical significance  were close to 0.5 which corresponds to a random classification. It is worth noting that both of these parameters were calculated from the GLCM matrix determined for the horizontal direction. Their counterparts counted for vertical direction GLCM were characterized by a significantly lower p-value and higher AUC  is low and the QUS parameters are subject to a large error. In extreme cases, the acoustic shadow makes it impossible to determine the lower edge of the tumor, thus making it impossible to determine areas for calculating internal and external parameters.The obtained results indicate that the signals received from the surroundings of breast tumours contain important information allowing for the classification of tumours as malignant or benign. Moreover, the majority of the individual parameters tested showed a higher classification efficiency when they were calculated for the tumour surroundings, and not for its interior. A similar relationship was observed in the case of multi-parametric classifiers, which is important in the context of the development of new cancer classification methods based on QUS techniques. In our research, the multi-parameter classifier, using a combination of internal and external parameters, was the best, although the best classifier using only external parameters was not significantly worse. It also suggests that external parameters are even more important and more valuable for classification than internal ones. In conclusion, we believe that quantitative analysis of peritumoral tissue can complement conventional US of breast tumours, thereby making it easier to diagnose breast lesions. The results of the study also show that alterations in peritumoral stroma caused by malignant neoplasms translate into changes in tissue properties that can be detected using ultrasonic quantitative techniques. These findings may be useful in research on the development and spread of cancerous tissue.


The claustrum is a narrow subcortical brain structure that resides between the striatum and insular cortex. The function of the claustrum is not fully described, and while our previous work supports a role for the claustrum in top-down cognitive control of action, other evidence suggests the claustrum may be involved in detecting salient changes in the external environment. The anterior cingulate cortex (ACC) and the anterior insular (aINS) are the two major participants in the salience network of human brain regions that activate in response to salient stimuli. While bidirectional connections between the ACC and the claustrum exist from mouse to non-human primate, the aINS connectivity with claustrum remains unclear, particularly in mouse. Here, we explored structural connections of the aINS with the claustrum and ACC through adeno-associated virus neuronal tract tracer injections into the ACC and aINS of the mouse. We detected sparse projections from the claustrum to the aINS and diffuse projections from the aINS to the borders of the claustrum were observed in some cases. In contrast, the insular cortex and endopiriform nucleus surrounding the claustrum had rich interconnectivity with aINS. Additionally, we observed a modest interconnectivity between ACC and the aINS. These data support the idea that claustrum neuron responses to salient stimuli may be driven by the ACC rather than the aINS. Many cortical areas bidirectionally connect with the claustrum across mammalian species, including primate , cat Le, rat Wh, and mouA frontal cortical area that would be expected to significantly connect with the claustrum is the anterior insular cortex (aINS), which, along with the claustrum, is implicated in salience detection . HoweverIn the mouse, the connection between the aINS and the claustrum is unexplored, while the anatomical boundaries of the claustrum are well-defined . As suchTo describe the mouse structural connections between the aINS, ACC, and claustrum, in this study we injected one adeno-associated virus (AAV) expressing a green fluorophore into the ACC and another AAV virus expressing a red fluorophore into the aINS, and vice versa. Both viruses exhibit both anterograde and retrograde trafficking properties . While wad libitum, and on a 12-h day/night cycle beginning at 07:00 each day. This study was reviewed and approved by the National Institutes of Health Guide for Care and Use of Laboratory Animals and the University of Maryland, School of Medicine Animal Care and Use Committee.Four C57BL/6J wild-type mice of both sexes were used for this study. Mice were 10–13 weeks of age at the time of surgery and euthanasia. Mice were group-housed with food and water available hSyn (human synapsin) promoter  or 100 nL of retrograde AAV expressing a td Tomato tag under the CAG (chicken beta-actin) promoter   . We contMice were overdosed on isoflurane and perfused with room temperature 0.1 M phosphate-buffered solution (PBS), pH 7.3, and then with ice-cold 4% paraformaldehyde (PFA) solution in PBS, 10 days after viral injection surgery. After extraction, the brains were post-fixed in 4% PFA solution overnight. Fifty micrometer thickness slices were obtained using the Integraslice 7550 MM vibrating microtome  and were stored at 4°C in 0.1 M PBS. The slices were mounted onto 25 × 75 × 1 mm frosted microscope slides  using 127 μL of ProLong Gold antifade reagent  as the mountant. The slides were imaged using a Nikon fluorescence microscope  with images obtained using both 4× and 10× magnification objectives. 10× images of slice sections were obtained and stitched together with 10% blending to create a complete field of view image of the entire brain slice. Adobe Illustrator was used to create chartings depicting viral tracer deposits.We followed the nomenclature of The core of the first viral tracer (rAAV2-retro-CAG-td-tomato) injection site Figure  spanned Labeling of claustrum neurons projecting to ACC spanned throughout the rostrocaudal and dorsoventral axes of the claustrum from AP = +1.54 to -0.58 mm Figures –C. RetroAnterograde labeling was also detected following viral injections . AnterogThe core of the aINS viral tracer (rAAV2-retro-JAWS-KGC-GFP) injection for B2.2 Figure  was in tFor the aINS injection, retrograde labeling was primarily concentrated in the superficial layers of the granular and agranular insular cortex Figures –6. The mFollowing aINS viral injection, dense anterograde labeling of axonal fibers were found throughout all layers of the granular and agranular insular cortex at all rostrocaudal levels (AP = +1.54 to -0.46 mm). Sparse anterograde labeling was also found in the granular and agranular insular cortex on the contralateral side of the aINS injection site. Moderate anterograde labeling of axonal fibers were found in the piriform areas and the endopiriform nucleus between AP levels +1.54 and +0.38 mm. Sparse anterograde labeling of axonal terminals was detected in the superficial layers of the ACC from AP = +0.62 to +0.50 mm. Lastly, there was sparse innervation of the border region of the claustrum on the ipsilateral side of injection site in select sections (AP level +0.98 to +0.14 mm).The viral tract tracer (rAAV2-retro-CAG-td-tomato) injection site core spanned all layers of the ventral and dorsal ACC and extended rostrocaudally from AP = +1.18 to +0.38 mm relative to bregma Figure . The penRetrogradely labeled neurons in the claustrum along the entire rostrocaudal and dorsoventral axes were present ipsilateral to the injection site from AP = +1.54 to -1.06 mm Figure . RostralAnterogradely labeled axonal fibers were detected at all rostrocaudal levels of the ACC (AP level +1.98 to +0.02 mm) following ACC viral injection Figure . AnterogThe viral deposit (rAAV2-retro-JAWS-KGC-GFP) core was centered primarily in the superficial deep layers of the aINS with parts into the deep layers of aINS and dorsal Pir1 Figures , 9. The Robust retrograde labeling from the aINS injection was observed in the deep layers of the agranular insular cortex Figure . This laDense anterogradely labeled axonal fibers were observed in the DP, dorsal tenia tecta 1 and 2, the ventral tenia tecta, the anterior olfactory  area Figure . AnterogFor cases B3.1 and B3.2 viral injections into the ACC and aINS were reversed with different fluorophores compared to cases B2.2 and B2.6. Virus expressing GFP (rAAV2-retro-JAWS-KGC-GFP) was injected in the ACC and virus expressing td Tomato (rAAV2-retro-CAG-td-tomato) was injected in the aINS. In addition, the ACC injection sites for these cases were on the contralateral side from the injection site in the aINS Figures  in orderIn both cases, the core of the ACC injection sites was in the deep layers of the ACC spanning from AP = +1.10 to +0.90 mm relative to bregma. In case B3.1, the ACC injection spread into the neighboring M2. In case B3.2, the penumbra from the ACC injection weakly spread into the superficial layers of M2.ACC viral injections resulted in retrograde labeling of claustrum neurons spanning from AP = +1.54 to -0.46 mm for case B3.1 and AP = +1.10 to +0.02 mm for case B3.2. Retrograde labeling was also observed contralaterally and ipsilaterally in the ACC relative to the injection site. For both cases, labeling was more prominent in caudal aspects of cingulate cortex in the deep layers (AP = +0.14 to -0.46 mm). Labeling was also observed in both superficial layers of the IL and DP regions in both cases. No labeling was found in the insular cortex in the rostral aspect of the claustrum for both cases . In case B3.2, anterograde fibers were also observed in the deep layers of the IL, but this was not apparent in case B3.1. Weaker anterograde labeling was found in the claustrum on the contralateral side to the ACC injection site. No labeled fibers were detected in the aINS in either case. No fibers were observed in the rostral aspect of the claustrum in case B3.2 and throughout the levels of the claustrum in case B3.1. Sparse fibers appeared at the boundary between claustrum and the deep layers of agranular insular cortex in the mid . Retrograde labeling density was relatively lower on the contralateral sides from injection sites. No retrograde labeling was detected within the claustrum boundary in the rostral level of the claustrum in case B3.1 and B3.2 . Moderate labeling of axonal terminals was present in the Pir areas, superficial layers of the IL, and the deep layers of S2. Both cases revealed anterogradely labeled fibers around, but not within, the claustrum boundary as defined by retrograde labeling arising from the ACC injection site. This was observed throughout the rostrocaudal axis of the claustrum in case B3.1 and 3.2  responds to relevant environmental stimuli . The antAlthough drawing comparative conclusions between rodent and primate aINS is challenging, and the present results should be interpreted accordingly, there are similarities between the aINS of the rodent and the non-human primate. For example, the granular, dysgranular, and agranular subdivisions of the aINS are conserved across species . In addiIn humans, multiple functional roles for the aINS are proposed, including an implication in awareness through a close integration with the dACC . AnotherFuture analyses of the connections between claustrum, aINS, and the ACC would benefit from being quantified. An anatomical quantification analysis that could be applied is exemplified in a study by HQ and BM designed the research. HQ generated the data. HQ, AP, SK, DS, and BM analyzed the data. HQ, SK, DS, CM, and BM wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The time synchronization protocol is indispensable in various applications of wireless sensor networks, such as scheduling, monitoring, and tracking. Numerous protocols and algorithms have been proposed in recent decades, and many of them provide micro-scale resolutions. However, designing and implementing a time synchronization protocol in a practical wireless network is very challenging compared to implementation in a wired network; this is because its performance can be deteriorated significantly by many factors, including hardware quality, message delay jitter, ambient environment, and network topology. In this study, we measure the performance of the Flooding Time Synchronization Protocol (FTSP) and Gradient Time Synchronization Protocol (GTSP) in terms of practical network conditions, such as message delay jitter, synchronization period, network topology, and packet loss. This study provides insights into the operation and optimization of time synchronization protocols. In addition, the performance evaluation identifies that FTSP is highly affected by message delay jitter due to error accumulation over multi-hops. We demonstrate that the proposed extended version of the FTSP (E-FTSP) alleviates the effect of message delay jitter and enhances the overall performance of FTSP in terms of error, time, and other factors. Time (clock) synchronization is a crucial requirement for the operation of wireless sensor networks (WSNs). Various applications, such as time-division multiple access (TDMA) scheduling, device tracking, monitoring, and data fusion, require all nodes to have synchronized clocks. A significant number of studies regarding this problem have been conducted in recent years. Many protocols and algorithms have been proposed to achieve sub-microsecond accuracy in experimental test-beds ,21,22,23In this study, we conduct a performance analysis on the Flooding Time Synchronization Protocol (FTSP)  and GradA comprehensive study of the performance of time synchronization protocols under diverse factors is performed. The effects of these factors on FTSP and GTSP were analyzed to understand the behavior of time synchronization protocols. The simulation methods used can be applied to the evaluation of future protocols.We propose an enhancement of FTSP (E-FTSP) and evaluate its advantages. We explain the problem with FTSP caused by the accumulation of jitter and describe how our improvement minimizes it. The simulation results prove that E-FTSP improves upon the performance of FTSP significantly, especially in large-scale multi-hop networks.The performance analysis is conducted to determine the diverse factors affecting the performance of time synchronization protocols, such as message delay jitter, time synchronization period, network topology, and packet loss ratio. In particular, FTSP is highly affected by message delay jitter and network size because message delay is accumulated as the number of hops from the reference node increases. To solve this problem, an extended version of FTSP (E-FTSP) is proposed and evaluated together with FTSP and GTSP from a diverse range of aspects. The main contributions of this study are as follows:The remainder of this paper is organized as follows. Related works are introduced in Evaluating the performance of time synchronization protocols in WSNs is a non-trivial task . There aSeveral studies ,27,28,29Regarding structure-based classification, time synchronization protocols can be grouped into two categories: centralized and distributed time synchronization . In centThere are two approaches to flooding: slow flooding and rapid flooding. FTSP is slow flooding-based because each node waits a predetermined amount of time to propagate its time information. However, several studies ,6,34 havIn contrast, rapid flooding was employed in PulseSync  to preveThe recently proposed Adaptive Value Tracking Synchronization (AVTS)  protocolAnother study  introducMeanwhile, distributed time synchronization protocols ,21,22,23Besides the message passing-based time synchronization protocols, recent studies have leveraged existing infrastructures, such as Wi-Fi beacons  and elecIn summary, numerous time synchronization protocols in WSNs have been proposed in recent decades. However, there has not been a quantitative performance study on the diverse aspects affecting the performance of time synchronization in WSNs. The aim of this study is to provide comprehensive insights into the operations, problems, and possible improvements in time synchronization protocols through a quantitative analysis, which considers a diverse range of factors that affect performance, such as message delay jitter, time synchronization period, network topology, and packet loss ratio of a wireless link.i can be modeled mathematically as follows:i. The difference between hardware clock rates is called the clock skew. Because the hardware clock operates continuously and should not be modified, a logical clock is defined to represent a global clock (synchronized clock). The value of the logical clock i is calculated as follows:i. The goal of the time synchronization protocol is to estimate In a wireless network, each node has its own local clock, typically referred to as a hardware clock. This hardware clock’s timing is calculated by counting the pulses of an oscillator operating at a particular frequency. However, the output frequency of each oscillator varies according to the hardware age or ambient environmental conditions . ConsequDesigning a time synchronization protocol for WSNs is very challenging. Most time synchronization approaches rely on message exchange between nodes. However, whenever a node generates a timestamp and sends it to other nodes for synchronization, the packet is subject to variations in delay before it reaches and is processed by receivers. Authors of FTSP analyzed the causes of delays and the magnitude of each in message transmissions . AssuminFTSP  is one oIn FTSP, when a node receives sufficient timing messages , it becomes a synchronized node and subsequently starts to forward the timing messages to other nodes. This design allows FTSP to synchronize in multi-hop networks. The root node is elected dynamically and can be re-elected in case of failure to ensure system robustness. The protocol does not build an initial tree; hence, it can adapt to dynamic topology changes. The experimental data indicate that the average synchronization error of FTSP is less than 3 µs per hop.GTSP  is desigThe performance of time synchronization can vary even when implemented on the same hardware platform . For exahttps://github.com/tinyos/tinyos-main/tree/master/tos/lib/ftsp) and GTSP  are referred from original implementation used to simulate these protocols. The MAC layer timestamp is implemented for both protocols. The Riverbed Modeler (OPNET)  is used The network size is 600 m × 600 m and the transmission range is set to 100 m, which allows distances of up to 12 hops in the network (grid topology 7 × 7) similarly to the evaluation of FTSP . In a raEach simulation execution is run for 2 hours (7200 seconds) and repeated 10 times. Different seeds are used in different executions to ensure that the random values are varied. However, it is important to note that in each particular simulation run, the simulation conditions  of each protocol are identical. The measurement data are collected after each synchronization round (30 s as default). message delay is defined as the time elapsed from when a node starts transmitting a message until the receiver finally processes it. This amount of time is accumulated by all nondeterministic delays  and the deviation in deterministic delays. Message delay is inevitable in network communications. However, it is difficult to determine or estimate the total delay time exactly. Consequently, the message exchange process suffers from variations in delay, known as message delay jitter.The term In this scenario, we wish to evaluate the effect of message delay jitter on the time synchronization protocols. This is a practical aspect of a real network, but it is typically ignored in a theoretical simulation ,15,23,41We analyze the effect of message delay on FTSP to obtain the cause and solution for this problem. We observed that the delay variation causes a fluctuation in the compensated drift. estimatedDelay is as below:offsetError is smaller than estimatedDelay, the nodes will regard the previous compensated drift as sufficient and a recalculation will not be performed. In this case, the node must only compensate for the offset (see lines 3–5 in Algorithm 1).If offsetError is larger than estimatedDelay, the nodes must calculate and compensate for the clock drift and clock offset using the algorithm of the original FTSP (see lines 7–9 in Algorithm 1).Meanwhile, if To prevent unnecessary adjustment of the compensated drift, we propose a mechanism to control the drift compensation process. The primary concept is that the nodes will decide whether they must compensate for clock drift whenever they receive a timing message. To implement this concept, the procedure to handle a received message with a newly defined variable estimatedDelay). Therefore, the nodes can reduce the skew fluctuation and computation overhead. This modification allows the nodes to skip updating the compensation of the clock drift if the offset error is trivial  then  4: //Compensate for offset only  5: compensate_offset(offsetError);  6:else  7: //Compensate for offset and drift  8: compensate_offset(offsetError);  9: compensate_drift;10:end ifestimatedDelay, we first analyze how FTSP stores the timing message in the regression table. i is calculated as:j, and i. Assuming that the clock drifts of nodes i and j remain constant in the short-term , the difference in hardware clock estimatedDelay value. estimatedDelay value of nodes that have the same hop distance to the reference node. The maximum delay in message delivery is 5 µs. The proposed protocol can estimate the maximum delay exactly with nodes that are received directly from the reference node (hop 1), and estimatedDelay slightly increases with further hops.To calculate estimatedDelay automatically, it is possible to predefine the value of this variable. In a homogeneous WSN, the maximum and average delays can be obtained through experiments and manually configured in the software. This allows for the complexity of the algorithm to be reduced while maintaining the efficiency of the proposed protocol. We continue the first scenario by comparing the performances of E-FTSP, FTSP, and GTSP in the presence of message delay jitter. In addition to calculating Choosing a proper synchronization period when implementing a time synchronization protocol is a trade-off problem. A short synchronization period allows a network to become synchronized quickly, whereas a long period allows the nodes to save energy. In this simulation scenario, the effect of the synchronization period of each protocol is analyzed from the perspective of synchronization speed (convergence time) and accuracy. We consider only the number of synchronization rounds instead of the amount of time for comparison. It is apparent from Regarding the synchronization error, it is interesting that the short period does not improve the accuracy of both protocols but slightly increases the error in FTSP as shown in Topology is an important factor that affects both the time synchronization protocols and other protocols in the network . The researchers that proposed FTSP and GTSP claimed that both protocols operate well in different topologies. In this scenario, we wish to demonstrate how different topologies affect the performance of FTSP and GTSP.FTSP in a multi-hop network has been reported to have poor performance. To understand the effect of hop distance from the reference node, the maximum hop distance between the reference node and the farthest node(s) is changed, as shown in Because GTSP is not affected by the position of the reference node, this subsection extends the previous simulation of GTSP to different topologies. We do not include FTSP in this scenario because the convergence of FTSP depends on the hop distance from the reference node, as demonstrated in the previous evaluation. Li  demonstrIn this subsection, we analyze the performance of FTSP, GTSP, and E-FTSP in term of network scalability, which is an important criterion for network protocols. Network scalability can be defined as the overall network performance may not degrade regardless of the network size and the number of nodes. Since the effect of the network density is discussed in In summary, FTSP has tendency that time synchronization error increases for the large-scale network while GTSP requires long convergence time proportionally to the network size. Therefore, neither are suitable for operating in large-scale networks. In contrast, the simulation results prove that E-FTSP provides high accuracy and fast synchronization regardless of network scale.Packet loss might occur frequently in a practical network, especially in wireless networks. Packet loss can be caused by many factors, such as collision during transmission, signal quality, hardware, and software issues . In thisMessage delay jitter can be considered to be the primary factor affecting the performance of time synchronization protocols. In particular, it causes fluctuations in the clock skew through multi-hop flooding and reduces the accuracy of FTSP significantly. An extended version of FTSP (E-FTSP) was proposed to reduce the effect of message delay jitter and it demonstrated outstanding performance compared to FTSP and GTSP, especially in a large-scale network.Regarding network topology, the position of the reference node affects the convergence time and synchronization error of FTSP and E-FTSP. In detail, the hop distance from the farthest node(s) should be as small as possible to achieve a high performance in FTSP and E-FTSP. Meanwhile, the distribution of nodes, especially the number of links between nodes in the network, affects the convergence time of GTSP. In detail, the convergence speed of GTSP increases with the number of links. In a small-scale network, there is no significant difference between the aforementioned protocols in term of synchronization error and convergence time. However, in a large-scale multi-hop network, FTSP has huge synchronization error, and GTSP has very slow convergence time. Meanwhile, E-FTSP provides more accurate and faster time synchronization regardless of network scale.Changing the synchronization period  does not reduce the synchronization errors of FTSP and GTSP. A short synchronization period slightly increases the synchronization error in FTSP. Interestingly, a short synchronization period reduces the number of rounds required to achieve convergence in GTSP. Time synchronization protocols require short intervals for fast synchronization and long intervals to save energy. Therefore, adaptive synchronization protocols should be further investigated.Packet loss clearly increases the convergence times and the synchronization errors of FTSP and GTSP. However, the network still achieves a synchronized status even when approximately one third of packets are lost. This proves the robustness of FTSP, GTSP, and E-FTSP.In this paper, we presented a performance analysis of time synchronization protocols under the effects of different factors, such as message delay jitter, synchronization period, network topology, and packet loss. From the simulation, our conclusions are as follows:Apart from accuracy, convergence speed, robustness, and scalability, other attributes of a time synchronization protocol, such as energy efficiency and time complexity of algorithms, also need to be evaluated before implementing them in a real system. Thus, it is expected that studies on these attributes will improve the feasibility of real systems.In our simulation, we did not evaluate the performance of FTSP and GTSP is mobile and the fast drifting environments. The mobility of the nodes in an ad-hoc network may generate unexpected results and problems. Although it has been claimed that FTSP and GTSP can operate well under dynamic topology changes, it is important to evaluate the performance of time synchronization protocols in a dynamic environment. These evaluations will be performed in future works. We believe that this study furthers our understanding of the performance of time synchronization protocols in real networks. Moreover, this work provides insights into the optimization of time synchronization protocols.