A non-intrusive method is presented for measuring different fluidic properties in a microfluidic chip by optically monitoring the flow of droplets. A neural network is used to extract the desired information from the images of the droplets. We demonstrate the method in two applications: measurement of the concentration of each component of a water/alcohol mixture, and measurement of the flow rate of the same mixture. A large number of droplet images are recorded and used to train deep neural networks (DNN) to predict the flow rate or the concentration. It is shown that this method can be used to quantify the concentrations of each component with a 0.5% accuracy and the flow rate with a resolution of 0.05 ml/h. The proposed method can in principle be used to measure other properties of the fluid such as surface tension and viscosity. Deep learning consists of several representation layers where each layer is obtained by the non-linear transformation of the previous layer2. Deep neural networks use the combination of these transformations to learn complex functions. In fluid mechanics, neural networks have been reported recently5 as tools that can help computational fluid mechanics simulations by mapping the estimates of low-resolution simulations to those with higher fidelity. In microfluidics, neural networks have been used to estimate various quantities for different applications9. Mahdi and Daoud10 used neural networks to predict the size of the droplets in an emulsion while Khor et al.11. used them to predict the stability of droplets in an emulsion. In our work, we employ neural networks to estimate fluid and flow parameters by observing the droplet formation process in a passive microfluidic chip. We extract this information by monitoring the flow of droplets with an optical microscope and training a neural network to obtain useful information from the recorded images. In one experiment, we trained a network to accurately measure the flow velocity at the inlet of the channel from droplet images. In a separate demonstration, a network was trained to identify the concentration of isopropanol in water in the droplet-forming solution. Our experiments demonstrate that DNNs can capture the complex nonlinear phenomena that result in the flow patterns and droplet shapes.Machine learning is a framework that learns from the data without being programmed. Deep leaning is a specific approach of machine learning12 and can change the size, generation frequency, and pattern of droplets13. Similarly, the dilution ratio of isopropanol (IPA) in water affects the generation frequency and flow pattern of the droplets. Such effects can also be recognized by the DNN to measure the concentration. There are alternative methods for measuring the flow rate17 or the dilution ratio21 in a microfluidic chip. The two prominent methods for on-chip flow measurements are the Coriolis method17, based on mechanical oscillations that depend on the flow rate, and thermal measurement16, in which a heater and thermometer are used to measure temperature changes in the flow. Commercial Coriolis devices are available with 0.05 ml/h accuracy while thermal devices typically have an accuracy of 0.012 in the range of 0 to 0.12 ml/h. A thermal flow rate sensor with accuracy as low as 0.0001 ml/h has been reported but for a very small range (0 to 0.09 ml/h). All these alternative methods require integration of additional complex devices on the microfluidic chip whereas the method we describe requires only an optically transparent window and an external camera. In addition, measuring the flow rate of two-phase flows is a challenging task. A correction technique is needed to be applied to the measured values of the flow rate sensor in order to have an accurate measurement24. DNNs can be used to measure the flow rate of the desired phase in a two-phase flow. For the measurement of concentration, the Coriolis device can also be used17 with up to 0.25% accuracy. Optical methods (refractometry)21 have been demonstrated to have excellent accuracy (as low as 0.01% concentration of IPA versus 0.5% for our demonstration); however, fine resolution optical refractometers generally rely on resonant mechanisms which are difficult to integrate into microfluidic chips. Optical probes and DNNs can be implemented to measure compounds concentrations for applications such as ammonium concentration measurement in oceanography25, hydrocarbon concentration measurement in reacting flows26, concentration measurement in microfluidic mixers28, and measurement of contaminant concentrations in water29. From a practical point of view, an interesting feature of the method presented in this paper is that a single mechanism can in principle be used for the measurement of a variety of different quantities, such as surface tension or viscosity, in addition to the ones demonstrated here. The challenge is to design the system and the training process such that the effects due to an individual parameter of the system dominate the pattern of the droplets.The flow rate is a parameter that affects droplet formationFigure Figure As we can see in Fig. Since the flow regime transition is due to a small change in concentration, the DNN cannot predict accurate values close to this point. However, the concentration at which the transition occurs can be relocated by changing the flow rate of the fluid. In this way the outlier points originating from these transitions can be corrected. We tested this idea by recording images of the flow patterns for six different flow rates and using them to train six DNNs independently. We tested the system by feeding six images of the droplet flows from the six flow rate sets to the corresponding DNNs to generate six concentration estimates. Maximum three outliers are removed to reduce the standard deviation of the predicted values. The closest value to the average of the remaining values is taken as the predicted value for the concentration. Figure 2 plasma treatment and possibly small dimensional variations. A possible solution is to train the DNNs using data from multiple PDMS devices and make the measurements insensitive to the change of chips.As a final evaluation, we tested the sensitivity of the DNN results with respect to the microfluidic chip. The experiment for the concentration measurement was repeated for a new microfluidic chip but with the same design. Unfortunately, the DNNs that were trained to estimate the IPA concentration using data from the first chip did not give accurate readings for the new chip. When new DNNs were trained with data from the new chip only, the performance obtained was practically the same as for the first chip . Using DNNs, the accuracy of the classification is drastically increased. Although image processing can be used to measure the droplets size, eccentricity, and velocity36. In these devices, it is important to measure the flow rate of the gas phase in order to determine their throughput and efficiency. Using DNNs is a suitable approach to measure the gas flow rate online. Moreover, DNNs measure the flow rate in the cases where the exact flow rate inside the microfluidic chip is unknown38. Furthermore, this method can be used to measure other fluid parameters. For instance, droplets in microfluidic channels are being used as the reaction media40. This application of droplet microfluidics requires the assessment of the droplet contents43. DNNs can be utilized for this purpose as chemical reactions change the properties of the droplets. Surface tension and viscosity measurements are other parameters that can be measured using DNNs. Many studies have investigated the effect of surface tension and viscosity on the droplet flow pattern46. In another approach, the changes in the patterns can be used in neural networks to obtain these influential properties.The method described in this paper can be used to measure the flow rate even when the flow rate is changing within the device. This change in flow rate happens for instance in micro-reactors where gaseous products are generated or consumed in fluidic channels47 is a recently developed approach that improves the repeatability of an experiment in a microfluidic device. By combining our approach with the control method of47, we can potentially maintain the same conditions of the training experiment and perform the measurement. In addition to this approach, we can train a DNN with inputs from a range of channel dimensions, temperatures, and pressures. A network trained with these inputs will predict more accurately because it has learned a range of parameter values. However, if the range of fluctuations increases, more complex design of the microfluidic device is needed to provide more information from the fluid to the network during the training.One of the limitations of our method is the sensitivity of the result to channel dimensions, pressure, and temperature fluctuations. These parameters should be the same during the measurement and during training to achieve highly accurate results. Intelligent control of fluid parameters using machine learning49, cell culture50, and electrochemical microreactors35.This network, in conjunction with a microfluidic chip, can be potentially used for the measurement of multiple fluid/flow properties at the same time. However, it should be taken into consideration that it is possible to reach the same result by changing two parameters separately. In this case, the information in the pictures is not enough to distinguish these parameters. The voting system used for the concentration measurement is an approach to prevent incorrect measurement under these circumstances. This system considers outputs from multiple networks and determines the correct concentration of the liquid by neglecting outlier output(s). In another approach, the addition of obstacles in the microfluidic channel and changing the geometries of the channels can force droplets to undergo different deformations, which would increase the information present in the images and especially be useful for surface tension and viscosity measurement. Fast and reliable readout of such parameters is important in many microfluidic applications such as chemical synthesis in multiphase flow systems52. We believe that many functionalities of these physical systems can be integrated into a compact on-chip imaging system54 that benefits from a pretrained learning algorithm to decode the rich information encoded in the images.We remark that although many fluidic properties can be measured using traditional sensors physically connected to the chip, their integration adds to the cost, size, and complexity of a microfluidic-based system. The presence of such cumbersome peripheral hardware is in fact what makes most of today’s microfluidic devices best described as “Chip-in-a-lab” instead of a “Lab-on-a-chip”A flow-focusing geometry is used to create droplets of water-IPA (Fisher Scientific UK) solution in silicone oil . The water-IPA mixture enters the main channel from the middle and the oil flows from the sides, as illustrated in Fig. For the concentration measurements, the IPA volume concentration in water is varied in 0.5% steps from 4% to 7%. For the flow rate measurements, the dispersed phase is a solution of 5.5% IPA in water. The flow rate of the water-IPA solutions is varied in 0.05 ml/h steps from 0.1 ml/h to 1.5 ml/h. Considering the precision of the pipettes and the syringes used, the accuracy of the IPA volume concentration in water is 0.08%. During the experiments, the inlet and the outlet temperatures were monitored and the mean temperature was 22.5 °C. The temperature variations were less than 1 °C during data acquisition, a negligible fluctuation which did not have a noticeable effect on the performance of DNNs.55 and an edge detection algorithm from Python’s scikit-image library (the standard deviation of the Gaussian filter is set to 1.2) are applied to the images to detect the droplet boundaries and remove the effect of the background and the light profile in the images. Image processing used in this paper is explained in more detail in the Supplementary Materials Section. This procedure is important to make sure that the DNN does not learn something related to the geometry of the channel or the light intensity. The processed images are used to train the DNNs. The structure of the DNNs is shown in the Supplementary Materials Section . The images are captured by a high-speed camera (Photron FASTCAM Mini UX100) and the image resolution is 1.8 μm/pixel. Images are recorded from the channel in a region downstream of the flow focusing geometry in which the channel dimensions are 100 μm by 1440 μm and 30 μm depth. The smallest droplet in experiments is 12 μm in diameter and is therefore fully resolved in the images. For each experiment, 4000 images are recorded at 50 frames per second. An image denoising algorithm from the OpenCV libraryion Fig. .Supplementary Information


Fusarium oxysporum and their presence was confirmed using spectrophotometer, transmission electron microscope (TEM), X-ray diffraction (XRD) and fourier transform infrared (FTIR) analyses. In order to carry out the conjugation study, capecitabine, tamoxifen, and paclitaxel were added dropwise to the GNPs solution under stirring condition and spectrophotometer, dynamic light scattering (DLS) and FTIR analyses were performed to prove the successful conjugation. Finally, AGS and MCF7 cell lines were used for methyl thiazol tetrazolium (MTT) assay to determine the toxicity of each drug and its conjugated form. Results showed that the spherical and hexagonal GNPs with maximum absorbance peak around 524 nm and average sizes less than 20 nm were produced. FTIR analysis clarified the presence of proteins on the surfaces of the GNPs. After the conjugation process although the FTIR analysis demonstrated that all the drugs were successfully conjugated to GNPs, MTT assay revealed that unlike the paclitaxel conjugated GNPs, capecitabine and tamoxifen conjugates displayed no toxic effects due to their deactivation and low half-lives. Moreover the average size and polydispersity index (PDI) of the GNPs after conjugation with all the three tested drugs increased. In conclusion different types of drugs could conjugate to the GNPs but it is important to employ high stable forms of the drugs in the conjugation procedure.Recent research tried to analyze the conjugation of some chemotherapeutic drugs to the biologically produced gold nanoparticles (GNPs) in one step, without the use of any additional linkers. GNPs was produced using  A successful treatment depends on the stage of the cancer and the response of the cancerous cells to the used strategies. Unfortunately, cancer drug resistance is one of the most critical problems in the cancer treatment which results in the negative prognoses2.Radio, chemo, and hormone therapies plus surgery are the main strategies for the treatment of different types of cancers. This multi therapy strategy is usually employed in order to increase the patient’s life. Unlike the use of different treatment strategies, many people around the world are suffered from cancer and are died yearly3. These drugs, like the many other ones have undesirable influences and toxic side effects on the normal cells4. Due to the side effects of the chemotherapeutic drugs, different drug delivery agents have been introduced and developed. Among them are Liposomes, dendrimers, nanoparticles, and nanotubes that are used as drug carriers5.Capecitabine, tamoxifen and paclitaxel are used for the treatment of different cancer types and induce high cytotoxicity to the different cancerous cell types6. Although this technique is very precise and attractive, there are some limitations such as the fact that the synthesis of the above mentioned materials is time consuming and expensive. Therefore, the alternative drug delivery systems are under consideration that chemotherapeutic conjugated nanoparticles are among them. Drugs conjugated nanoparticles have higher half lives in the body than the non-conjugated forms and due to lower lymph drainage and higher blood circulation in the tumor site, the drug loaded nanoaprticles are accumulated in that region and are more toxic for the cancerous cells than the normal ones7.Sometimes, these drug delivery vehicles are designed to be selectively attached to the cancerous cells. For this aim, some especial ligands have been conjugated to the surfaces of the drug delivery vehicles so that the drug is delivered through ligands and the specific receptors of the cancerous cells8. Their utilization is safe and they have anti-angiogenic properties. GNPs are among the high-Z agents (Z = 79) that have a high X-ray absorption ability and when they enter into the body they increase the radiation sensitivity. Therefore, high energy form from the irradiation is concentrated inside the tumor, and increases the efficiency of the radiotherapy. Hence lowered doses of the radiation are used in vivo2.If the chemotherapeutic conjugated nanoparticles have radio sensitization properties, their effects are improved. The best example of this type of drug delivery system is the gold nanoparticles (GNPs). GNPs attract the attentions due to their low toxicity, biocompatibility, and the ability to bind to the different types of the molecules10.GNPs have a high surface area that results in their high ability of binding to different molecules and having a high half-life in the body. Therefore, they attach with high payload to the chemotherapeutic drugs, decrease the requirements for administration of high doses of the drugs and decrease their toxic effects for non-cancerous cells in the body11 which in comparison to the other types of nanoparticles production, is safe, inexpensive, fast and does not release any harmful and toxic byproducts in the environment12. In the biological technique of GNPs nanoparticles production, there are several bacterial and fungal strains that can produce this type of nanoparticles13. In this method the microbial proteins play the role as reducing and capping agents of the produced nanoparticles that made them stable even when they are in the close contacts15. In the conjugation process these proteins actively bind to the drugs without the requiring of any additional linkers. It seems that these proteins help the nanoparticles to enter into the cancerous cells16.Among the different methods of GNPs production, is biological techniqueDue to the insufficient data about the assessment of the conjugation possibility of the chemotherapeutic drugs to the biologically produced GNPs, recent research has tried to analyze the conjugation of three different chemotherapeutic drugs, capecitabine, tamoxifen and paclitaxel, with two different physiological forms, liquids and tablets, to the biologically produced GNPs without the use of any additional linkers. The conjugation of the drugs to the GNPs was proved by different methods, and then their anti-cancer activity was evaluated against two different cancerous cell lines. The results from this basic study will be beneficial for the future studies.Fusarium oxysporum (PTCC 238-21-3) was purchased from the Pasteur Institute of Iran and used for extracellular GNPs production. The fungal strain was cultured in a flask containing 150 ml of Sabouraud’s dextrose broth (SDB)  in the incubator at 30 °C for 7 days. The obtained mycelia were separated using centrifugation process at 6000 rpm for 10 min and the fungal culture supernatant was used for the production of GNPs. For this aim, 100 μl of 1M HAuCl4  solution was added to 100 ml of the fungal culture supernatant . The negative control flask that contained 100 ml of the sterile SDB plus 100 μl of 1M HAuCl4 was prepared. The flasks were incubated at 40 °C, 200 rpm, for 24 h. The first sign of GNPs formation was the color changing of the reaction mixture from yellow to dark purple or red18.2O. In this regard, GNPs were centrifuged under 14,700 rpm for 30 min. The pellet was suspended in ddH2O and the above mentioned process was repeated twice. Finally, the pellet was suspended in ddH2O and sterilized using tyndallization method. Briefly, the suspension was heated indirectly under water steam for 30 min and incubated overnight at 37 °C. The process was repeated three times15.Because the GNPs in the fungal culture supernatant contained some impurities that may interfere with the conjugation process, the mixture was washed using ddH2O. The used wavelength ranges were from 450 to 650 nm19.If the GNPs are produced, they have maximum absorbance peak around the wavelength of 500–550 nm due to the surface plasmon resonance (SPR) of the nanoparticles. Therefore, the optical density (OD) of the color changed fungal culture supernatant was achieved using spectrophotometer. The used blank was ddH20.The size and shape of the produced GNPs were analyzed using Zeiss Leo 910 transmission electron microscope. For this aim, 5 μl of the color changed fungal culture supernatant was placed on a carbon coated copper grid and after 15 seconds the excess of the solution was removed and the grid was air dried and analyzed under TEM20.XRD was performed to confirm the presence of the elemental gold in the fungal culture supernatant. For this aim, the sample was freeze dried and used for XRD analysis. The diffracted intensities were obtained from 30° to 80° at 2°θ using a Philips automatic X-ray diffractometer−1 10.In order to determine the structure of the GNPs and prove the presence of capping proteins on the surfaces of the nanoparticles, FTIR analysis was applied. For this aim the sandwich method was performed and because the sample was in the aqueous form, the AgCl plates were used. A small drop of the sample was placed on one of the plates and the second plate was placed on the top. After achievement a thin layer of the sample, the plates were placed into the sample holder and FTIR spectrum of the GNPs was measured by the use of Nicolet FTIR instrument in the range of 500–4000 cm2O, separately21 and concentration of them were determined using UV-visible spectrophotometer. The standard curve obtained from each pure drug concentration versus OD used from the previous research articles. According to Harini et al. capecitabine has two maximum absorbance peaks at 308 and 616 nm22 and according to Albert et al. tamoxifen has one maximum absorbance peak at 236 nm23. Therefore for capecitabine 616 nm and for tamoxifen 236 nm wavelengths were used in the UV-visible spectrophotometer analysis, the used blank was ddH2O and the concentration of each drug was determined based on the obtained OD.Capecitabine (500 mg), tamoxifen (10 mg) and paclitaxel (6 mg/ml) drugs were purchased from OSVE, Iran hormone and Sobhan pharmaceutical companies, respectively. 100 mg of the tablet powder of capecitabine and tamoxifen were dissolved in 100 ml of ddH2O. The above mentioned procedure was repeated twice10.For drug loading experiment, 0.5 ml of each drug stock solution was added drop wise to 50 mL of GNPs solution in the sterile condition. The mixtures were maintained for 72 h at room temperature under stirring condition. In order to remove the unbound drugs from the solution, the mixtures were centrifuged at 15,000 rpm for 10 min and each pellet was suspended in ddH2O and the wavelength ranges were from 400 to 650 nm24.The maximum absorbance peaks of the drugs conjugated GNPs after conjugation process were changed. Therefore, ODs of the drugs conjugated GNPs were achieved by the aid of the spectrophotometer. The utilized blank was ddH25.The size distribution of the GNPs before and after conjugation with the tested drugs was obtained using the Zetasizer Nano range instrument . For this aim each drug conjugated solution and GNPs were sonicated for 60 min and analyzed at room temperature. The Z-Average (d.nm) and polydispersity index (PDI) of the solutions and the zeta potential of the GNPs were determined−1. The non-conjugated drugs were utilized as the controls24. The samples were in the liquid form and their preparation method was described earlier.In order to determine conjugation of the drugs to the GNPs, FTIR analysis was employed. For this aim, FTIR spectra of the drugs conjugated GNPs were measured by the aid of Nicolet FTIR instrument in the range of 500–4000 cm26 and MCF7 cell line was sensitive to tamoxifen27 and paclitaxel28. For both of the cell lines Dulbecco’s Modified Eagle’s medium  which was enriched with 10% fetal bovine serum  and 1% penicillin streptomycin solution  was used as a working medium.In order to analyze the drugs conjugated GNPs cytotoxicity, two different cancerous cell lines, AGS (ATCC CRL-1739) and MCF7 (ATCC HTB-22), were purchased from the Pasteur Institute of Iran. AGS cell line was sensitive to capecitabine2. The obtained 80% monolayer cells achieved and then the medium was removed and the cell surfaces were washed by the use of phosphate buffered saline (PBS). Then, 200 μl of the working medium was added to all the wells. The experiments started from the second well of the second row of the plate. In order to avoid the evaporations, the margins of the plate were not used. In the plate that was seeded with AGS cell line, capecitabine, capecitabine conjugated GNPs and GNPs were tested. Each row of the plate was filled by one of the mentioned samples. In this regard, 200 μl (1 mg/ml) of the sample was added to the second well and the solution was mixed by the use of pipeting. Therefore the concentration of the sample in the second well was 0.5 mg/ml. Then 200 μl of it was transferred to the third one and the obtained concentration of the sample was 0.25 mg/ml. This procedure continued until the 10th well and 200 μl of the 10th well was discharged. Therefore the concentration of the sample in the 10th well was 0.0019 mg/ml. The 11th well was a control and contained 200 μl of the working medium. By this technique each sample was diluted by half. For the plates that were seeded with MCF7 cell line, two rows of the plate were loaded by tamoxifen, tamoxifen conjugated GNPs and two rows of the plate were loaded by paclitaxel and paclitaxel conjugated GNPs. One row was filled with GNPs alone. The tests were carried out in duplicates and all the plates were incubated at 37 °C for 24 h in the presence of 5% CO2. In the next day, all the wells of the palates except the margins wells were filled with 10 μl of 5 mg/ml MTT dye solution. The plates were incubated at 37 °C for 4 h in the presence of 5% CO2, the solution was removed and 100 μl of dimethyl sulfoxide  was added to all the wells. The plates were incubated for 20 min and the ODs of the wells were measured using ELISA reader at the wavelength of 570 nm30. Finally, the IC50 value for each sample was analyzed and compared with the obtained IC50 values from the others. The percentages of the viable cells were calculated from the obtained ODs31.Before the cytotoxicity assay, GNPs and all the drugs conjugated GNPs were frieze-dried and the obtained powder of them were weighted. 1 mg of each powder was dissolved in 1 ml of the working medium (the concentration of each sample was 1 mg/ml). Eight of test samples: GNPs, each of the tested drugs conjugated GNPs and each of the tested drugs (with the concentration of 1 mg/ml in the working medium), were analyzed by methyl thiazol tetrazolium (MTT) assay that utilized 3--2,5-diphenyltetrazolium bromide dye  solution. For this aim, 8000 cells/well were seeded in each well of the 96 wells micro titer plate in the presence of the working medium and incubated at 37 °C for 24 h in the presence of 5% CO2. The obtained 80% monolayer cells achieved and then the medium was removed. For the wells that were seeded by AGS cell line, 700 μl of the working medium was added to each well and one well was filled with 700 μl (1 mg/ml) of capecitabine conjugated GNPs and the other one was filled with 700 μl (1 mg/ml) of GNPs. For the wells that were seeded by MCF7 cell line, 700 μl of the working medium was added to each well. One well was filled with 700 μl (1 mg/ml) of tamoxifen conjugated GNPs, the other one was filled with 700 μl (1 mg/ml) of paclitaxel conjugated GNPs and the last one was filled with 700 μl (1 mg/ml) of GNPs. The plate was incubated in the above mentioned condition for 3 h and the medium was discharged. The surfaces of the cells were washed trice using PBS and the cells were detached using trypsin-EDTA solution . In order to quantify the concentrations of the gold in the cytoplasms of the tested cells ICP-OES 730-ES, Varian instrument was employed in the presence of the standard graph31.In order to analyze the amounts of the GNPs and drug conjugated GNPs that entered into the used cell lines, ICP study was performed. For this aim, 10000 cells/well were seeded in a 6 wells micro titer plate. Two wells were seeded with AGS and three were seeded with MCF7 cell lines. The cells were incubated in the presence of the working medium at 37 °C for 24 h in the presence of 5% COin vitro stability of the drugs was analyzed by incubation the drugs conjugated GNPs (200 µl) with the equal amounts of the working medium at room temperature for 24 h and 2 weeks. The samples were analyzed using visible spectrophotometer. The utilized blank was ddH2O and the wavelength ranges were from 400 to 650 nm24.In order to analyze the stability of the drugs conjugated GNPs the samples were kept in the room temperature for 6 months. The The color of the fungal supernatant in contrast to the negative control, turned from yellow to red due to the SPR of the GNPs. Figure Visible spectral analysis result showed that the GNPs sample had maximum absorbance peak around 524 nm. Because the sample had high turbidity, it was diluted 1:15. Figure The results revealed that the produced GNPs had spherical and hexagonal shapes with the average sizes less than 20 nm. Figure 3. Its reference code was 03-65-2870. Figure XRD results confirmed the presence of the elemental gold in the fungal culture supernatant. GNPs had cubic crystalline form with the calculated density of 19.28 g/cm−1 indicated the presence of OH group, and its wideness was due to the acidic OH group. The poor peaks presented in the ranges from 3500 to 3400 cm−1 were due to the stretching vibrations of NH bands that represented the presence of the amide bands of the protein. The spectrum available at wave number of 1500 cm−1 had two branches that one branch belonged to C=O, and its lateral peak belonged to the bending vibration of the NH group that represented the amide bands. The spectrum available at wave number of 1400 cm−1 represented the CH2 band. The peaks that observed below the wave number of 1000 cm−1 indicated the metallic bands. Figure In order to determine the structure of the GNPs, FTIR analysis was used. The obtained results revealed that different types of proteins surrounded the GNPs which indicated the presence of capping agents on the surfaces of GNPs. The peak at wave number of 3400 cmThe concentration of the tablet form drugs was determined using UV-visible spectrophotometer. Based on the obtained ODs, the concentration of capecitabine was 54 μg/ml and for tamoxifen was 47 μg/ml.Three different drugs  conjugated GNPs were analyzed using the spectrophotometer instrument. The results demonstrated that although the ODs of the GNPs after conjugation were altered and their maximum absorbance peaks were shifted to higher wavelengths, these maximum absorbance peaks were limited to 500–550 nm. Figure DLS results revealed that GNPs before and after conjugation process had different sizes and dispersities which confirmed the changes in the surface of the GNPs due to the GNPs-drugs core shell formation. Totally, as it can be seen in Fig. −1 indicated the presence of OH of the alcoholic and phenolic groups. The peak at wave number of 1600 cm−1 belonged to C=O that its location shifted due to its presence in the ring and its placement next to the nitrogen atom. The peaks of the nitrogen bands observed in this area. The peaks below the wave number of 1000 cm−1 related to the different groups that presented on the ring. For capecitabine conjugated GNPs, all the peaks of the drug  observed in the spectrum. At wave number of 1600 cm−1 the carbonyl group of the drug shifted down due to its placement near the nitrogen of the capping proteins. In addition, all the peaks related to the nanoparticles such as the peak at wave number of 3400 cm−1 which belonged to OH group indicated in the obtained FTIR results, which demonstrated that the GNPs without any fractures bound with the drug and formed core shells.As it was described earlier, in order to prove the conjugation of the drugs to the GNPs, each of the drug and the drug conjugated GNPs was analyzed by the use of FTIR. Figures −1 indicated the presence of OH of the alcoholic and phenolic groups. The peak at wave number of 1600 cm−1 belonged to C=O that its location shifted due to its presence in the ring and its placement next to the nitrogen atom. The peaks of the nitrogen bands observed in this area. The peaks in the ranges from the wave numbers of 1000 to 1100 cm−1 belonged to the C–O bands. For tamoxifen conjugated GNPs, the peaks of the metallic bands presented. Also the peaks in the ranges from wave numbers of 1000 to 1100 cm−1 that belonged to the drug C–O bands displayed. It is possible to prove the presence of the drug with the peak area at wave number of 1600 cm−1 that belonged to C=O and nitrogen bands (it confirmed the presence of the types I and II amides). The peaks in the ranges at wave numbers of 1600 to 3700 cm−1 belonged to the carbonyl and amide II groups that were in the GNPs which indicated that the GNPs without any fractures bound with the drug and formed core shells.In case of tamoxifen as demonstrated in Fig. −1 indicated the presence of ester bands which were in the formulation of the drug. The width peak at 3400 cm−1 indicated the presence of OH group of the alcoholic and phenolic structures. This peak was weak due to the formation of hydrogenic bands with the other drug components. The peaks below the wave number of 1000 cm−1  and the ones around wave number of 1600 cm−1, demonstrated the positions of the substitutions on the aromatic rings. The powerful peaks that observed at wave number of 1600 cm−1 were attributed to C=C bands of the aromatic rings. The spectrum available at wave number of 2900 cm−1 had two branches that illustrated the SP3 hybrids in CH bands. For paclitaxel conjugated GNPs, in the obtained spectrum all the paclitaxel peaks specially the ones that were seen at wave numbers of 900 and 1600 cm−1 showed. Also the drug ester, CH and C=C bands were observed. The peak at 3500 cm−1 which belonged to the amide II group of the GNPs was more visible. The carbonyl peak of the GNPs also demonstrated, resulting that the drug bound to the GNPs and formed core shells.For paclitaxel, as indicated in Fig. 50 was in the 4th well with the concentration of 0.125 mg/ml. Also for tamoxifen in the MCF7 cell line, the drug was deactivated and the cells could grow from the second well of the plate. For GNPs and tamoxifen conjugated GNPs their IC50s were equal and were in the 3th well with the concentration of 0.25 mg/ml which indicated the toxicity of the tamoxifen conjugated GNPs was due to the toxicity of the GNPs and not the drug.In order to analyze the cytotoxicity of each drug conjugated GNPs and each of the drugs, two different cancerous cell lines, AGS and MCF7, were employed and the tests were carried out in duplicates. As it was mentioned previously, the analyses started from the second well of the plate. MTT assay revealed different results for each group of the specific drug conjugated GNPs, GNPs and the used drug. The results are indicated in Fig. 50 value for paclitaxel was calculated in the 6th well with the concentration of 0.031 mg/ml and for paclitaxel conjugated GNPs was in the 7th well with the concentration of 0.0156 mg/ml. Paclitaxel conjugated GNPs induced more toxic effects than the drug and GNPs alone. Finally, GNPs induced toxic effects in both of the used cell lines but more toxic effects in the AGS cell line was observed than the MCF7 one. Figure Paclitaxel and paclitaxel conjugated GNPs in the MCF7 cell line showed toxic effects in the cell culture that the ICIn order to analyze the amounts of the GNPs that entered into the used cell lines, ICP study was performed. As it was mentioned in the materials and methods section, all the wells of the plate was filled with 700 μl of 1 mg/ml of each sample. Therefore both of the cell lines received the same concentration of each tested sample. The results indicated that GNPs could penetrate to the AGS and MCF7 cell lines but the amounts of them in the cells were different. The amount of the GNPs that entered into the AGS cell line was 1.1 ppm and into the MCF7 cell line was 2.53 ppm. The amounts of the capecitabine conjugated GNPs, tamoxifen conjugated GNPs, and paclitaxel conjugated GNPs in the used cell lines were 1.15, 0.77 and 0.07 ppm, respectively. Therefore, the ability of the GNPs in the same concentration in entering to different cells after conjugation with the drugs was different. Figure in vitro stability analysis of the drugs conjugated GNPs showed the same results. Therefore the GNPs after conjugation with the drugs had high stability in the environmental and in vitro conditions. The obtained maximum absorbance peaks for GNPs and drugs conjugated GNPs are shown in Table The results of the stability test of the drugs conjugated GNPs after 6 months of the experiments showed that the obtained maximum absorbance peaks had small variations which confirmed the drugs conjugated GNPs were stable at room temperature. Furthermore the 32. Tamoxifen is used for the treatment of early and advanced breast cancer33 and paclitaxel is used for the treatment of metastatic breast, leucopenia, ovarian, liver, prostate, lung and neck cancers34. It is important to note that the tested cell lines in the current study were susceptible to the used drugs. Based on the literature review, AGC cell line was susceptible to capecitabine26 and MCF7 cell line was susceptible to both of the tamoxifen27 and paclitaxel28 drugs.Capecitabine, tamoxifen and paclitaxel are three different chemotherapeutic drugs that are utilized for the treatment of different cancers. Capecitabine is used for the treatment of gastric, gastroesophageal, esophageal and colorectal cancers2. The second method is acknowledged to be inexpensive, so the recent research attempted to use GNPs for conjugation with the above mentioned drugs. By this method, drugs were conjugated to the GNPs with high affinity and therefore, the doses of the used drugs in the chemotherapy procedure were decreased. It means that the side and toxic effects of the drugs were lowered35.In the chemotherapy procedure, the use of these drugs had some disadvantages such as the appearance of resistance cancerous cells and their undesirable side effects in the patient’s body which may be the main reason of the patient’s death. In order to overcome these disadvantages, there are some solutions such as application of different drug delivery agents that purposefully target the tumor cells and conjugation of the chemotherapeutic drugs to some especial types of nanoparticles30. Recently, due to the toxicity of the nanoparticles that are produced by chemical and physical techniques, the biological method of nanoparticles production is under more considerations. In the biological technique, different types of bacterial and fungal strains produce the nanoparticles actively in or out of the microbial cells which depends on the placement of the cell’s enzymes and active component36. Some microorganisms have the ability of the production of both types of nanoparticles. A fungal strain, F. oxysporum is among them. It was indicated that this nonpathogenic fungus has a high ability of production of different types of nanoaprticles due to its powerful secretion systems and is used widely in the field of nanoaprticles production19. In the nanoparticles production procedure the fungal proteins and enzymes play a role in reducing and capping the produced nanoparticles that made them stable even when they are in the close contacts19. GNPs are among the different nanoparticles that are produced by this fungal strain. The biologically produced GNPs are acknowledged as the most biocompatible type of nanoparticles that induce low toxic effects in the cell culture19. They have a high surface area and high ability for binding to different types of bio-molecules. The binding of the bio- molecules to GNPs are directly accomplished without requiring any additional foreign linkers due to the presence of the capping proteins on the surfaces of the produced GNPs16. There is a paucity of research about the conjugation ability of the chemotherapeutic drugs to the biologically produced GNPs. Kumar et al. have used resveratrol as the anticancer agent for conjugation with the GNPs that were produced by a bacterial strain and found that the drug conjugated GNPs acted better in the cell culture than the drug alone24. Therefore recent research attempted to analyze the conjugation of three different chemotherapeutic drugs, capecitabine, tamoxifen and paclitaxel, with two different physiological forms, liquids and tablets, to the biologically produced GNPs without the use of any additional linkers.Chemical, physical and biological methods are three main techniques of nanoparticles production37. The extraction of nanoparticles that are produced out of the cells is easier than the ones that are produced in the cells37; therefore, in this study the extracellular production way was selected. After the production of GNPs, their presence was confirmed by the aid of spectrophotometer, TEM, XRD and FTIR analyses. In the spectrophotometer analysis, GNPs had maximum absorbance peak around 524 nm due to their SPR. If the nanoparticles form clusters, the maximum absorbance peak shifts to the higher wavelength or this peak is not be observed38. Therefore, this test indicated that the GNPs were produced. TEM and XRD results proved the presence of round nanoparticles with the average sizes less than 20 nm that they had at least four distinctive peaks in the XRD resulted spectrum. DLS results revealed that the Z-Average of the monodisperse nanoparticles was 54.27 ± 3.1 , their dispersities were acceptable and this factor was not changed drastically.The FTIR results revealed that all the three types of the drugs were successfully conjugated to the biologically produced GNPs. Totally, the FTIR spectrum that was obtained for each drug conjugated GNPs illustrated the peaks of the GNPs and the ones with the especial used drug that indicated the successful conjugation of the drugs to the GNPs. Therefore, it can be concluded that three used drugs with different chemical compositions and different half lives, even if they are deactivated, can conjugate to the GNPs which is due to the high binding affinity of GNPs to different types of molecules.24. Before the MTT assay, each sample (i.e. drug conjugated GNPs and GNPs) was freeze-dried, weighed and with the concentration of 1 mg/ml was used for MTT assay. Also the drug powder was weighted and used with the same concentration. Although all the samples were used in the same concentrations, the weight of each drug in the drug conjugated GNPs sample was lower than the drug sample alone. This is because of the removal the unbound drugs and also presence the weight of the GNPs in the conjugated samples. Although the exact amount of each drug in the drug conjugated GNPs sample was lower than the non conjugated form of the corresponded drug but in the case of paclitaxel the drug conjugated GNPs had better anticancer activity than the drug sample. It is recommended to calculate the exact amount of the unbound drugs after washing the conjugates and use the equal amount of it that is banded to the GNPs in the drug tested group.In order to remove the unbound drugs from the solution, the conjugates were washed. By this process the unbound drugs could not affect the results of the cytotoxicity assay50s were 0.125 mg/ml and 0.25 mg/ml, respectively. This may be due to the inherent differences between these two different cell lines. In the assessment of the capecitabine and capecitabine conjugated GNPs, the results expressed that both of them were deactivated in the cell culture. Although GNPs induced some toxic effects in the cell culture, after conjugation with the drug they did not induce any toxicity in the AGS cell line. Probably, the GNPs functional toxic groups were covered with the used drug. This was coincidence with the increase of the Z-Average of GNPs after conjugation with capecitabine (83.51 ± 5.5 nm). ICP results illustrated that GNPs in both of the conjugated and non conjugated forms could penetrate into the cells, therefore, if the drug was active, it would induced high toxicity in the cell culture because the nanoparticles could pass the drugs from the cell surface to the cytoplasm.As it was mentioned previously, in the case of MTT assay each set of the drug, drug conjugated GNPs and GNPs indicated different results. The GNPs in both of the AGS and MCF7 cell lines showed toxic effects that their toxicity was higher for AGS than the MCF7 cell lines. Their ICTomuleasa et al., used cisplatin, doxorubicin, and capecitabine conjugated GNPs and expressed that GNPs increased the susceptibility of cancerous cells to the used conjugated drugs. Despite the present study, the chemically produced GNPs in addition to L-Aspartate as a chemical linker were used. In the previous study, capecitabine did not lose its activity due to the use of low incubation time (about 1 h) for conjugation to the GNPs.38. Moreover, once again, because of the nature of the tamoxifen, like the capecitabine, the drug was deactivated.In the assessment of tamoxifen and tamoxifen conjugated GNPs, unlike the capecitabine tested group, after conjugation of the drug to the GNPs, the functional groups of the GNPs did not fill and therefore, GNPs in the conjugated and non conjugated forms could induce the toxic effect in the MCF7 cell line (IC50s = 0.25 mg/ml). ICP results showed that GNPs in both of the conjugated and non conjugated forms could penetrate into the cells but in contrast to the conjugated form, the penetration of its non conjugated form was better. This may be due to the attachment of the drugs on the surfaces of the nanoparticles, and formation of core shells with bigger sizes which affects its penetration into the cells. Again, this was coincidence with the increase of the Z-Average of GNPs after conjugation with tamoxifen (61.91 ± 2.1 nm). As the spectrophotometer data revealed, although for all the three used drugs conjugated GNPs the maximum absorbance peaks were shifted to higher wave numbers, but the peaks were limited between 500–550 nm which was an indication of remaining of the GNPs in their nano sizes even after conjugation with the drugs50s were 0.0.0156 mg/ml and 0.031 mg/ml, respectively. ICP measurement revealed that both of the GNPs and drug conjugated GNPs could penetrate into the MCF7 cell line but the penetration of the drug conjugated GNPs was lower than the GNPs alone. The DLS results confirmed this hypothesis because the Z-Average and PDI of the GNPs after conjugation with paclitaxel increased. Therefore, the drug conjugated GNPs with lower penetration than the GNPs could induce higher toxic effects and its toxic effect was not due to the toxicity of GNPs alone.In the paclitaxel tested group, the drug preserved its stability and both of the drug and drug conjugated GNPs indicated toxic effects in the cell culture. As it is observed in the Fig. 40 that have used chemical linkers which banded to the drugs, the one step and easy linking of the functional groups of the drugs to the capping agents of GNPs is one of the most important advantages of this method. One of the disadvantages of the present study was the time that was spent for conjugation process . This linking time was not optimized and it needs more assessment to lowering the conjugation time.Recent research has tried to analyze the conjugation possibility of three different chemotherapeutic drugs to the biologically produced GNPs without the use of any additional linkers. The production of the biologically produced GNPs is easy, safe, eco friendly and fast. By this method some microbial secretions such as proteins act as the reducing and capping agents. In our study the capping agents that their presence on the surfaces of GNPs was confirmed by FTIR method, acted as the linker which leaded to one step conjugation of the drugs to the GNPs. Therefore in comparison with the other studiesIn future, it is recommended to use especial ligands in conjugation with GNPs that are specific for the receptors of the utilized cancerous cell lines. It is recommended to decrease the time and use cold incubation for conjugation process and to use the other types of stable drugs.2O. The results from this paper showed that bigger sizes of GNPs after conjugation with different drugs achieved which affected on their cellular penetration. Although the conjugated form of the drug-GNPs had bigger sizes and lower penetration to the cells but they could induce better anti cancer activity than the non conjugated drugs. By the use of the biologically produced GNPs, the conjugation method was easy and inexpensive without the use of any additional linkers. Results from the current study suggest that the paclitaxel conjugated GNPs may has therapeutic potential for treatment of the breast or other types of cancers.In conclusion, the biologically produced GNPs had the ability of conjugation with different types of chemotherapeutic drugs but it is very important to choose the drugs which preserve their activity in the linking process. Our findings revealed that although all the three used drugs could conjugate to the GNPs, the drugs which were in the tablet form were non toxic in the cell culture. This was due to their deactivation and instability after dissolving in ddHIn future, it is recommended to use especial ligands and drugs in conjugation with GNPs that are specific for the receptors of the used cancerous cell line.


The currently accepted interval of weekly cast changes in the treatment of clubfeet seems unsubstantiated. A force sensor is needed to determine the adaptation rate of a clubfoot to establish what cast change interval would be most effective and efficient. We developed a force sensor based on the principle that the resonance frequency of an LC-tank changes when a metal target is brought in close proximity. A thin rubber ring between the LC-tank and the metal target transformed this proximity sensor into a force sensor. With a static load test and an incremental load test, the performance of the constructed force sensors was characterized. The custom-made sensor showed excellent sensitivity (( A clubfoot is a congenital deformity of the foot which is commonly treated with the Ponseti method . In thisTo measure the adaptation rate of the clubfoot, a force sensor is needed with very specific requirements. The specific application that is described above requires a sensor that is thin enough to fit underneath the cast without causing any damage to the skin, accurate enough for a measurement over the period of one week and its power should be supplied by a small battery that would be safe in a portable device for a baby. The requirements used for the development of the force sensor described in this paper are summarized in Numerous force sensors with varying functional principles are commercially available, but, to the best of our knowledge, none of them are suitable for long-term precision measurements within a portable setting. Other sensors do not fit our requirements because they are either too bulky or experience too much drift. OEM load cells  are too large for our requirements, and it would be challenging to make a low-power amplifier with high resolution for load cells based on strain gauges. Force Sensitive Resistors  are susceptible to drift ,3,4. OptThe aim of this paper is to describe the performance and accuracy of a new inductive force sensor for the application of long-term force measurements in a portable setting.LHR_DATA, a digital value that is proportional to frequency, with 28 bits of measurement resolution. The resonance frequency is influenced by the distance of the coil to any metal target. In most applications, a non-ferromagnetic and conductive metal  is used to modulate the inductance of the coil. Closer proximity of the target to the coil will increase eddy currents in the target, thereby decreasing the inductance of the coil. In close proximity, the relation between distance and resonating frequency can be linearized with small errors. The exact functioning and examples for sensor design can be found in the documentation provided by Texas Instruments e.g., [An inductive force sensor was developed based on the evaluation kit of the inductance to digital converter LDC1000  and its successor LDC1614 . This ints e.g., .To convert this proximity sensor into a force sensor, an elastic medium with a known stiffness was added between the coil and the target. Our initial approach consisted of a simple sandwich of these three components. Although this solution has been researched by others , and patIn the final design, a base-plate was added and the rubber medium was shaped into a “ring”, enclosing the PCB see . With thThe terminology used for the sensor, the acquisition unit (AU), and their combination as a sensing and logging unit (SLU) throughout this paper are presented in LHR_DATA over time. The AU was equipped with the LDC1614 [The sensor was connected to a custom-made AU to log  LDC1614  and a KL LDC1614  that wasCHx_RCOUNT was set to maximum (0xFFFF) for increased precision at the expense of bandwidth, SETTLECOUNT_CHx to 50  compresses the sensor, thereby changing the oscillation frequency. LHR_DATA, proportional to this frequency, is measured and stored by the AU. None of the parts used in close proximity of the sensor were made of metal to prevent any interference with the LC-tank. A hanger of 127 g was used as a pre-load to which weights of 100 g each were attached , filtered with a high-pass filter (0.01 Hz). Furthermore, the noise performance was characterized by measuring the Allan deviation [eviation  (p. 15) Temperature drift of the SLU was defined as the change in LHR_DATA per change of the room temperature . It was calculated as F) was constant, in counts/Time drift of the SLU was defined as the relative change of LHR_DATA over time, after a 30 s settling time. Two calculation methods were used:Drift was calculated as the gain of the best linear fit of the signal over logarithmic time, in %/log(h) .Drift was calculated as the relative difference between the initial value and the final value after several specific times .The SLU was loaded with incremental 30-second-long steps of 100 g each up to a maximum of 1 kg. With this test, the following characteristics were determined:Sensitivity of the SLU was defined as the change of the output signal per change of input parameter (Non-linearity was defined as the greatest deviation to this linear fit [DATA/ΔF) . It was near fit .Accuracy of the SLU was defined as the greatest deviation to a polynomial fit that was used to allow conversion of LHR_DATA to force, as percentage of the full scale output.Hysteresis of the SLU was defined as the maximum difference between the loading and unloading curves, as percentage of the full scale output.LHR_DATA for the static load tests were corrected for an apparent start-up effect and for the influence of temperature changes using the identified temperature drift.The collected data were processed using Matlab R2017b. In post-processing, the In its intended application in force measurements in the treatment of clubfoot, the SLU uses a logging protocol of four 30 s periods per hour to preserve battery power. Every time the SLU is activated an initial start-up effect is observed, as shown in The static load test was performed with 9 sensors and the incremental load test with 13. Typical examples of the resulting data of both tests are presented in The influence of temperature was (−0.088 ± 0.030) N/°C (The time drift of the SLU was determined to be (2.1 ± 0.7) %/logThe noise characteristics are presented in the Allan deviation graph in The greatest sources of error are hysteresis  and drift  was usually applied longer than the other loads. Due to the time drift, this might have lead to an overestimation of the calculated hysteresis.Preliminary results of measurements in the treatment of habitual toe walkers, Dupuytren fingers , and cluA practical small-sized force sensor has been developed for long-term precision measurements in a portable setting. The used approach of inductive sensing combined with a compressible medium shows good applicability for the proposed force measurements between the skin and a cast, and great potential for many other applications.


This work investigated in Alzheimer’s disease dementia (AD), whether the probability of making an error on a task (or a correct response) was influenced by the outcome of the previous trials. We used the antisaccade task (AST) as a model task given the emerging consensus that it provides a promising sensitive and early biological test of cognitive impairment in AD. It can be employed equally well in healthy young and old adults, and in clinical populations. This study examined eye-movements in a sample of 202 participants ; 95 control participants). The findings revealed an overall increase in the frequency of AST errors in AD and MCI compared to the control group, as predicted. The errors on the current trial increased in proportion to the number of consecutive errors on the previous trials. Interestingly, the probability of errors was reduced on the trials that followed a previously corrected error, compared to the trials where the error remained uncorrected, revealing a level of adaptive control in participants with MCI or AD dementia. There was an earlier peak in the AST distribution of the saccadic reaction times for the inhibitory errors in comparison to the correct saccades. These findings revealed that the inhibitory errors of the past have a negative effect on the future performance of healthy adults as well as people with a neurodegenerative cognitive impairment. There is growing interest in the potential of the paradigm as a measure of inhibitory control in healthy and clinical populations. If we can uncover the major underlying factors that give rise to these errors of control in the AST, we will be in a better position to understand how these errors might be mitigated in various clinical and non-clinical populations to improve cognitive control. The AST presents a stimulus to the left or right of the screen whilst the observer is required to avoid the natural impulse to look towards the stimulus, and instead to look away, to the opposite side of the screen. Healthy adults fail to inhibit the impulse to look towards the target on approximately 20% of trials2. The failure rate is substantially higher in AD (55–75%)4. In healthy participants, the error is usually followed shortly afterwards by a self-correcting saccadic eye movement, whereas these corrections occur infrequently in AD dementia, and then only after a substantial delay. The frequency is positively correlated with the severity of the dementia and memory impairment5. To date, no study has attempted to track the moment-by-moment changes in cognitive control in AD dementia during the AST. Could there be a mechanism that resets or reboots after each error so that the system starts afresh on the current trial? Or is the outcome of the current trial subject to the influence of the past events? A simple way forward would be to examine the effect of past trials on the present trial performance. In this context, we investigated the extent to which the AST errors might be influenced by the outcomes of the previous trials. Tatler and Hutton6 conducted the only study to examine the effects of historical antisaccade trials on the current trial in a sample of young healthy participants. This work revealed that an inhibition error on a preceding trial was strongly associated with an increase in the probability of an error on the current trial. Interestingly, the effects were not limited to the immediately preceding trial. They found a linear and cumulative effect of the number of past error trials on the probability of an error occurring on the current trial. It is not known whether this error phenomenon is also found within a clinical cohort.How do we stop ourselves doing something that we know we should not be doing, and how do our previous errors influence our current behaviour? A convenient way of addressing these questions in the laboratory employs the antisaccade task9, the on-line control by neural and behavioural error detection and correction networks (i.e. adaptive networks) operates primarily in the context of the conscious awareness of the task conflict. However, according to Miller and Cohen10 the neural network that includes the Anterior cingulate – Dorsolateral Prefrontal Cortex projections, is activated by the task conflict that is detected with or without conscious awareness. Tatler and Hutton6 found, using the AST, that the error rates in fact increased, rather than decreased, on the trials that followed an error on the previous trial. This seemed to provide little support for either of these cognitive control models. They concluded that “…an increased probability of making an error following an error on the previous trial is opposite to the predictions made by the popular conflict monitoring account of antisaccade performance”p394. However, a key prediction of these models relates to the issue of whether or not the conflicts and errors are detected, whether consciously or unconsciously. As mentioned by Tatler and Hutton6, within a healthy (young) population there is little variability in the detection of the errors and conflict; error correction in the AST is close to 100%. Thus, young healthy participants typically show high error detection rates, as revealed by the very high proportion of corrective saccades. Therefore, whilst it is surprising that AST errors increased following a previous error, their analysis was not a critical or devastating test of the models. A more critical test for the models would contrast the effects of the trials in which the errors are detected, with the trials where the errors are not successfully detected and corrected. The cognitive control models would predict that the errors that are detected and corrected (either consciously or unconsciously according to Miller and Cohen10) should lead to a lower probability of errors on the following trial, in comparison to the errors that are not detected and corrected. This prediction can be best examined using participants with Alzheimer’s Disease dementia and MCI, as they are known to generate a relatively high proportion of both corrected and uncorrected errors. This prediction cannot be easily examined with healthy control participants who produce relatively few uncorrected errors in the AST.Several influential models of cognitive control have proposed that behavioural errors are detected on-line, by neural monitors (such as the Anterior Cingulate cortex) and used to adjust and correct performance on a moment-by-moment basis. According to Dehaene and colleagues11? Answers to these questions will provide a more comprehensive insight into inhibitory control as a candidate biomarker for MCI/AD and in the monitoring of the progression of the disease.Therefore, the following questions were addressed in this study: Do the past errors and successes have an effect on AD performance on the current trial? Are the inhibitory errors mediated by the speed of the reaction time on the current trial2 = 2.400, p = 0.301, Cramer’s V = 0.077), but did differ on mean age  = 13.469, p < 0.01). As expected the dementia scores on MOCA differed between the groups  = 63.715, p < 0.001); where the AD group had lower dementia scores on the MOCA, compared to the controls & MCI; the MCI group had lower scores than the controls: Table The mean and standard deviation of the participant demographic and cognitive characteristics are presented in Table 2 = 1.778, p = 0.411, Cramer’s V = 0.066). This resulted in an overall total of 4732 out of a possible 4848 trials, whereby the frequency of incomplete sequences was independent of the cognitive groups . 79.2% of participants performed the full set of 24 trials and more than 95% of participants completed over 20 trials. We conducted a complete case -analysis  and found no substantial differences in the overall pattern of the effects.One hundred and sixty participants performed the complete sequence of 24 trials. Others had shorter sequences with no lower than 11 trials. These participants were balanced across the groups : and errors with no correction (Error − c) by participant group. The AD and MCI participants have a significantly higher proportion of Error + c and Error − c responses on the antisaccade trials in comparison to the control group . The distributions of the AD and MCI were overlapping, consistent with the view that the MCI can be considered as a preclinical AD group. There was a significant effect across groups for Error − c responses with respect to the proportion of error trials:  and in relation to total number of observations (i.e. “Errors” + “Correct”): .The antisaccade data can be classified into 3 types of response outcomes. Table Table An analysis was conducted to determine the potential impact of the Error + c trials in comparison to the Error − c trials. Table Generalised Linear Multilevel Model Analyses (GLMM) were conducted to investigate the potential impact arising from participant variability. The fixed effects of the best model contained the cognitive groups and the outcome of the previous trial. Inclusion of an interaction between these was not significant . The inclusion of an age fixed effect was also not significant , indicating that the participant random effect captured the imbalance within the data. The error probability estimates  on the previous trial influence the outcome on the current trial? Is there a cumulative effect of the past errors (or correct saccades) on the current behaviour? What is the relationship between AST errors and saccade reaction times? The findings were clear and compelling. A previous error or a consecutive sequence of errors increased the probability of an error on the current trial. Conversely, a correct saccade on the previous trial increased the probability of a correct response in the future. The effects were cumulative with longer sequences of errors (or corrections) predicting a proportionally higher probability of future errors (or corrections). The current findings are consistent with the previous work on healthy young adults, which reported a similar effect2 . Interestingly, the errors were significantly reduced on the trials that followed a previously corrected error, compared to trials where the error remained uncorrected, revealing a level of adaptive control in participants with early dementia. This indicates that participants with early (MCI) or a later stage of dementia (AD) retain a level of adaptive cognitive control that may help to modulate the errors. This effect was not significant for the control group when analysed in isolation, probably due to the well-known low level of uncorrected errors in healthy controls.A growing number of studies have demonstrated that endogenous saccadic eye movements (i.e. antisaccades) are highly sensitive to the impairment of cognitive control in the AD group, and the reflects to some extent the dysfunction of working memory and inhibitory control11. The signal that first reaches the threshold “wins” the competition and triggers the saccade. The threshold of activation for either of the two competing signals can be modified by modulating the rate at which activation of the neural signal rises to the threshold, or the baseline level of activation before the stimulus onset15. Therefore, one possibility is that there is a persistent and stable increase in the baseline level of activation in the exogenous (i.e. prosaccade) and/or a decrease in the baseline of the endogenous (antisaccade) signal across the trials6 giving rise to the increased errors. In this way, with each additional error in the sequence, a growing tendency emerges towards further errors. Relatively fast reaction times are associated with the errors (i.e. prosaccade) in comparison to the correct, but slower antisaccade, as revealed in Fig. 16.It is worth considering the potential factors that could have given rise to the cumulative effects of the AST errors. According to the race competition models of the AST a “race” commences, at the onset of the target, between the exogenously triggered prosaccade and the endogenous antisaccade signal17. A number of studies have shown that individuals find it difficult to complete two consecutive self-control tasks that require cognitive effort; the first is completed as usual, but there is usually a decline in performance on the second task18. One influential theory19 states that this decline stems from a depletion in the limited resources of self-control, since the effect is specific to self-control tasks and not found for automatic tasks20. Since glucose was claimed to be the source of this critical energy, we hypothesised that a glucose supplement should be able to mitigate against this decline in self-control21. Our results did not support this theory. Using a double-blind study, we found that glucose did not protect the antisaccade performance following a Stroop task. However, there was a strong effect of the motivation level on the AST errors. High levels of motivation were associated with a low frequency of AST errors. Therefore, given that the motivation levels mediated the AST errors in the Kelly et al.17 study, it is conceivable in the current work that motivation levels may also have been associated with the frequency of errors. One can imagine a descending cascade where an error leads to the depletion of motivation, which then leads to a further error. This cascade could be a mediating factor in the accumulative effects of the AST errors. A consecutive sequence of successful correct saccades may have protected against the decline in task motivation, in contrast to a series of failures, which would be more likely to have fostered a lower level of motivation. Clearly, further work will be required to evaluate the validity of this hypothesis.Another possible explanation can be derived from our work on the effects of glucose and motivation on antisaccades22. The sessions are kept as short as possible to protect against the loss of concentration and motivation. We use a blocked presentation of trials design and we started each testing session with the AST to avoid the well-known priming effects from the prosaccade task. Note also the selective nature of the eye-tracking impairments in AD. For example, participants with AD dementia performed remarkably similar to controls in an attentional disengagement saccade overlap task23 which demonstrates that prosaccadic eye movement paradigms are unlikely to be inherently cognitively demanding for these participants. This increases our confidence that the patients understood the antisaccade task and were motivated to perform well, given the evidence that on a high proportion of the trials, the errors were self-corrected.Inferences that are derived from the neurocognitive work on Alzheimer’s disease have often been contingent on a number of assumptions in relation to the participant’s performance on the cognitive task. For example, there is an assumption that the participant has a full understanding of the task and is sufficiently motivated to perform well. A major appeal of the saccadic eye movement paradigms is that these can be applied irrespective of a verbal or somatomotor dysfunction, and therefore can be used for the assessment of cognition across a wide range of domains including clinical, animal neurophysiological and infancy research23. The current work revealed that inhibitory control in AD was influenced by the past performance in a similar fashion to the healthy controls. Similarly, errors were associated with faster reaction times in a similar manner to the control group. These findings revealed that there is no fundamental change in the principles that govern the regulation of AST errors in people with dementia. Apparently, the dementia status adds a positive constant to the AST errors but there is a common underlying principle in relation to the effect of past error and success on the current behaviour.In considering the impairment of saccadic eye movements in AD group it is important highlight again its selective nature given that a number of keys features were in fact, well preserved. For example, prosaccade amplitudes, peak velocity and the attentional disengagement (measured by the GAP effect) were preserved and remain stable over time24. The following criteria were required for the diagnosis of MCI25: (1) a subjective complaint of a decline in memory function ; (2) an objective impairment using a formal memory test or other cognitive impairment (where the scores on the standard cognitive tests were at least 1.5 SDs below the age-adjusted adjusted norms); (3) a relative preservation in the activities of daily living. The following exclusion criteria were applied: a previous history of head trauma, stroke, cardiovascular disease, active or past alcohol or substance misuse/dependence, or any physical or mental condition severe enough to interfere with their ability to participate in the study. All participants had the capacity to consent to participation in the study and gave signed informed consent. The study received ethical approval from the local NHS Research Ethics Committee, NHS Health Research Authority and Lancashire Care Teaching Hospital Trust. These experiments were performed in accordance with the UK British Psychological Society ethical guidelines.Two hundred and two participants were included in this study: 42 with dementia due to Alzheimer’s disease (AD) and 65 with mild cognitive impairment (MCI); Control participants were 95 volunteers who were either the spouses of the AD or MCI participants or were research volunteers from the North West of the UK. Participants with dementia were referred and diagnosed via memory clinics in the United Kingdom National Health Service after a comprehensive evaluation by the dementia assessment team. The participants were all white British or European with a minimum of 11 years of education and fluent in the English Language  and excessively delayed saccades (due to inattention) using a temporal window of 80–700 ms measured from the onset of the target.Eye movements were recorded using the EyeLink Desktop 1000 eye-tracker (SR Research) sampling at 500 Hz. Participants were seated 55 cm away from the display monitor (60 Hz). The Miles test ) was used to determine the dominant eye and tracked accordingly. The raw data was exported from the EyeLink to the DataViewer software and analysed offline with bespoke softwareEach trial was preceded by a 1 second instruction screen stating that the participant should look away from the target. A central fixation target was displayed in white on a black background. This was displayed for one second. There was a 200 ms blank interval before the appearance of the saccade target. The saccade target (in red) was then presented in a random order 4 degrees away from the fixation target on the left or right side for 2 seconds. Participants were asked to fixate on the central fixation spot and then to generate the saccade to the opposite side of the screen as soon as the target appeared on the display  is considered to be within the dementia range.The Dementia Assessment: Montreal Cognitive Assessment28 assesses phonological memory function; participants were presented verbally with a set of single digit numbers and required to recall the digits in the correct serial order. The number of digits gradually increased during the course of the experiment, starting with two and going up to a maximum of eight. Two trials were presented at each level of difficulty and a participant must get both correct in order to progress to the next level. Participants were then presented with a reverse digit span task, where they were required to name the items in the reverse order of the presented sequence. Again, the number of digits were presented incrementally, from two to eight. Two trials were presented at each level, both needed to be correct in order for the participant to progress. A total digit span score was then calculated out of 16 for each of the forward and reverse digit spans tests.The digit span task29 participants were presented with a board containing an irregular array of nine square blocks. The experimenter pointed to each block, one block at a time in a given pseudo-randomised sequence. At the end of the sequence, the participant was required to point to each of the blocks that were indicated in the correct serial order. The number of square blocks selected in a sequence was increased over the course of the experiment, from two up to a maximum of eight. Two trials were presented at each level and the participant had to achieve both correct in order to progress to the next level. A reverse version of the task was also conducted with the participant indicating the sequence in the reverse order. A total spatial span score was then calculated out of 16 for each of the forward and reverse forms of the tests.In the spatial span task32.The principal aim of these analyses was to search for any evidence that the outcome of the current trial (error vs. success) and/or the reaction times was predicated on the outcome of previous trial(s), the participant’s cognitive grouping or another explanatory variable. It is important to take into account each participant’s predisposition for a particular outcome. Any given set of participants may have very different natural error rates irrespective of their group membership or demographic status. Accounting for specific effects at the level of each individual recognises that the trials are nested within a participant, and consequently enables a better quantification of the main effects of interest. This was achieved using multilevel models, also known as hierarchical linear model or random coefficient modelsi,j denote the trial outcome for participant i at trial j, which describes a Bernoulli trial that takes the value 1 for an inhibitory control error and 0 for a correct saccade. The trial probability for an AST error pi,j, is mapped to the real-line using the logit transform, i,j as the log of the reaction time for participant i at trial j. The reaction times are transformed onto the log-scale to reduce the asymmetry in the measurements. These measurements can be described using the linear regression model i,j is the residual error associated with each observation. The predictors i represents participant i’s deviation from the overall baseline value, β0. These subject effects are assumed to follow a zero-mean normal distribution with unknown variance to be estimated. All of the analyses were conducted in R statistical software33 using the “lme4” package35 for inference for the linear and generalised linear multilevel models (GLMM). The additional package “survival”37 was used to evaluate and summarise the reaction time survival curves in Fig. Two dependent variables for the eye-tracking tasks were entered into these analyses: the trial outcome (i.e. whether or not an inhibitory error was committed in the AST: error or correct); and the saccade reaction times after the appearance of the “distractor” for the errors and the correct responses. Let y


This data article includes the datasets of the mean and the standard deviation of ice thickness in a set of sailing zones for a sailing route that goes through the Northern Sea Route (NSR) between Murmansk and Pusan. The route under consideration is between the longitudes 33° 45′ 0″ and 129° 3′ 60″ and the latitudes 69° 24′ 27″ and 35° 6′ 0″ that correspond to the ports of Murmansk (Russia) in the west and Pusan (China) in the east respectively. Within this area, the part that is between the longitude 57° 0′ 0″ and −168° 58′ 0″ and the latitude 70° 27′ 18″ and 69° 6′ 0″ correspond to the NSR. This route has been divided into 49 subzones, and each subzone into squares of 12.5km of side following the data structure of the database Copernicus [1].The detailed coordinates of the subzones (longitude and latitude) are provided in the article. The daily ice thickness for the period between January 1, 2006 and December 31, 2016 has been obtained for each of the 12.5km sided squares. This data article provides the normality test outcomes and the corresponding p-value of the ice thickness data for each subzone on each calendar day. Moreover, the mean and the standard deviation of the ice thickness in each subzone are also provided. The data provided in this data article can be very helpful for researchers for different applications related to the weather conditions in the NSR zone or to shipping related issues. For instance, the data provided in this paper can be used to investigate the change in ice thickness in the NSR over the period 2006–2016 and to estimate future changes. Another potential application is the estimation of the need for icebreaker assistance as well as the possible ranges for the vessel sailing speed based on the vessel type and for any navigation day in any of the NSR zones. In addition, this data can be used to estimate the risk of blockage for any vessel type because of ice conditions in the NSR zones. It can be helpful to estimate the economic viability of shipping through the NSR since the icebreaker assistance, the speed and the risk of blockage have an effect on the profitability of the shipping lines that may use the NSR. The first dataset contains the coordinates of the 49 subzones of the considered route. The second dataset includes two MS Excel files that provide information about the normality test of the daily ice-thickness of all calendar days and the corresponding p-values for 49 subzones based on data from 2006 to 2016. The third dataset contains two other MS files that provide information about the mean and standard deviation of ice-thickness for all calendar days and for the same 49 subzones.22.1.The selected sailing route is between the port of Murmansk in Russia in the west and the port of Pusan in China in the east. The route includes the NSR, that is divided into 7 administrative zones by the Russian Administration. Moreover, to obtain more accurate data about ice thickness, the route has been divided into 49 subzones. The coordinates of these subzones, as well as the travelled distance within each subzone, are provided in 2.2parfor" loop of Matlab. The download request through the website server took between 20 seconds and 2 minutes per file on every computer, with the majority of the cases being in the low range (close to 20 seconds per file) which corresponded to a total download time of around 11 days.Each subzone defined in The downloaded files are in the format .nc (NetCDF). They result from the TOPAZ system based on an advanced sequential data assimilation method and the Hybrid Coordinate Ocean Model (HYCOM version 2.2). The dataset includes 26-years Arctic reanalysis product in the period 1991–2017 included. The variables delivered are all physical variables, including 3D currents, temperatures and salinities, 2D parameters for sea ice, mixed layer depth and sea surface heights. Sea surface temperature and sea surface heights are corrected for bias, with an online bias correction algorithm. It is worth noting that ARCTIC_REANALYSIS_PHYS_002_003 is a set of data coming from observed data and a numerical model to fill the gap. It is a merged product of weekly sea ice thickness (SIT) measurements in Arctic from the satellite CryoSat-2 altimeter and the satellite Soil Moisture and Ocean Salinity (SMOS) radiometer (referred to as CS2SMOS). This product is gridded with a resolution of approximately 25 km The total size of the downloaded files is 24.79 GB. The detailed file sizes per zone are shown in Matlab has built-in function to read the NetCDF files. However, for the data files that have been included in this data paper, this functionality was able to read part of the data correctly but not the ice thickness data. Therefore, to avoid any risk of having incorrect data, NetCDF4Excel macro was utilized after modifying it to be connected with Matlab. We therefore, converted the NetCDF files into MS Excel files. To do so, we modified the Excel Macro (NetCDf4Excel) so that it opens the NetCDF files without requiring the user to browse for the file. Instead, this macro was modified to require the file name only while calling it, i.e. without browsing. Matlab has then been used to call the Macro in an automatic way and to feed it with the file name so that the file can be read in Matlab and then saved as an MS Excel file. The file names have been generated in an automatic way in Matlab in order to fit with the file names that are given by the database Copernicus. The complete process has then been done automatically. Indeed, the file names are generated automatically based on the zone number and the date. Each MS Excel file is stored in the folder that belongs to its zone. An iterative approach (using loops) has been used to automate the process. In addition, the NetCDF4Excel macro asks the user to browse and select the .nc file and then the user needs to save the converted file manually to MS Excel (.xlsx format). To automate things, this NetCDF4Excel macro has been modified by replacing the "browse" command with a variable that contains the file name and path. In addition, a code has been added to automatically save the converted file to .xlxs file. Excel is called using the command actxserver ('Excel.Application'). This command creates a Microsoft Component Object Model (COM) that can be used to control MS Excel through Matlab.2.3First, some of the ice thickness data that were downloaded had small very small negative values . The downloaded data have then been processed in order to eliminate these negative values by setting all the negative values to be equal to zero.0 if the data are normally distributed or 1 for H1 if the data are not normally distributed) and the corresponding p-value. To perform the test for a given subzone and a given date, the ice thickness data of each square of 12.5 km of side in the subzone and for the considered date have been first obtained and averaged. For example, for 1 January, the average ice thickness data of 1/1/2006 , the average of 1/1/2007, 1/1/2008 … etc. have been calculated. Therefore, 11 means (from 11 years) have been obtained for each subzone. The Lilliefors test has then been used on these 11 data points for each day and each subzone. The corresponding outcomes are two 365 by 49 cells MS Excel files that are provided in the supplementary materials of this data paper and are described in Given the variability of the ice thickness data, normality test has been conducted. Lilliefors test was performed using the Matlab function lillietest  that returns the selected hypothesis (0 for HIt is worth noting that out of 17,885 tests, for 5,253 tests the null hypothesis was rejected which means that for the majority of the tests the null hypothesis was accepted and therefore the ice thickness data can be considered as normally distributed for each subzone and each calendar day.2.4Based on the normality tests, the mean and standard deviations of the ice thickness for the 11 values described in section 2.5To download the data using python, motu client package is required.