The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness’s plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task and a demanding issue to measure the snowpack’s liquid water content (LWC) and its temporal evolution with conventional in situ techniques. We propose an approach based on the use of time-domain reflectometry (TDR) and CS650 soil water content reflectometers to measure the snowpack’s LWC and temperature profiles. For this purpose, we created an easily-applicable, low-cost, automated, and continuous LWC profiling instrument using reflectometers at the Cooperative Remote Sensing Science and Technology Center-Snow Analysis and Field Experiment (CREST-SAFE) in Caribou, ME, USA, and tested it during the snow melt period (February–April) immediately after installation in 2014. Snow Thermal Model (SNTHERM) LWC simulations forced with CREST-SAFE meteorological data were used to evaluate the accuracy of the instrument. Results showed overall good agreement, but clearly indicated inaccuracy under wet snow conditions. For this reason, we present two (for dry and wet snow) statistical relationships between snow LWC and dielectric permittivity similar to Topp’s equation for the LWC of mineral soils. These equations were validated using CREST-SAFE in situ data from winter 2015. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Additionally, the equations seemed to be able to capture the snowpack state . Lastly, field test results show advantages, such as: automated, continuous measurements, the temperature profiling of the snowpack, and the possible categorization of its state. However, future work should focus on improving the instrument’s capability to measure the snowpack’s LWC profile by properly calibrating it with in situ LWC measurements. Acceptable validation agreement indicates that the developed snow LWC, temperature, and wetness profiler offers a promising new tool for snow hydrology research. Snow wetness is used as an indicator of snow melt and snow instability [Seasonal snow is an influential reservoir constituent in the hydrological cycle that discharges temporarily stored freshwater to the forelands ,2. Downstability . An incrtability ,8. Quanttability ,9, and ctability ,11,12. Atability ,14,15.Generally, snow wetness is quite difficult to measure in situ. Furthermore, to account for the spatiotemporal evolution of meltwater runoff and snow instability, non-destructive and continuous snow LWC monitoring is necessary because changes in the LWC can rapidly alter various snowpack properties and its meltwater outflow ,16. MoreTechel and Pielmeier  and Boyn3). Moreover, Lundberg concluded that continuous registration of snowpack wetness with acceptable spatial resolution (approximately 5 cm) was possible to achieve with several sets of probes—mounted with 3 cm vertical and 5 cm horizontal spacing—combined with a multiplexer and a storage unit to record the data. Additionally, a more recent study by Waldner et al. [The aim of this study is to: (1) test the capability of the CS650 soil water content reflectometer to provide reliable snow LWC measurements and (2) provide a non-destructive, non-invasive, low-cost, automated, and continuous alternative to in situ snow LWC measurements at different snowpack heights above the soil surface by creating a snow wetness profiler (SWP) setup using TDR and a series of CS650 water content reflectometers. The determination of LWC using TDR is based on the relationship that exists between the relative complex dielectric constant of the medium and its water content. The first use of TDR to determine LWC was executed in soils by Topp et al. ,34,35. Lr et al.  makes usr et al. , Tiuri er et al. , and Fror et al.  to calibr et al.  make user et al.  to derivr et al. . Ultimatr et al. ,42 demonTo our knowledge, there is no existing literature on the usage of the CS650 TDR sensor to provide snow LWC measurements. Furthermore, most of the snow TDR studies mentioned previously have been conducted manually, not in automated, continuous fashion, nor has an in situ season-long, field-withstanding instrument that provides the LWC and temperature profiling (at different depths above the soil surface) of the snowpack simultaneously been developed. Therefore, the SWP will be a novel contribution as an alternative to execute year-round, automated, undisturbed in situ snow LWC measurements, and provide insight on the snowpack state  at different snowpack heights above the soil surface. The SWP was developed using two arrays of low-cost  and -power consuming, easily-assembled  and –installed  moisture reflectometers. SWP observations are automated and continuous.Snow Thermal Model (SNTHERM) LWC simulations forced with in situ meteorological data were used to evaluate the accuracy of the instrument due to the lack of LWC in situ observations at the study site . In orde3·s−1, monthly averages from as low as 1.5 (January/February) to 16 (April) m3·s−1, and an annual average of ~ 4 m3·s−1) (https://nwis.waterdata.usgs.gov) to local rivers.In situ observations were performed in the county of Caribou in the state of Maine located in the Northeast of the United States of America. The coordinates for the study area are 46°55′ N, 68°01′ W . The sitThe Field Snow Research Station  is operated by the NOAA Cooperative Remote Sensing and Technology Center (CREST) in the City University of New York (CUNY). The field station is located within the premises of the Caribou Municipal Airport  and in close proximity to the National Weather Service (NWS) Regional Forecast Office. The station was established in 2010 to support studies in snow physics and snow remote sensing. The surrounding area in direct contact with the instruments at the station is covered in grassland.The station tallies a total of twenty-two (22) instruments that provide continuous and automated all-year round measurements of the physical characteristics of snow, soil temperature down to 20 cm into the ground, and all basic meteorological parameters. The list of measured snow pack physical properties includes: snow depth, SWE, snowfall, and the snowpack temperature profile. Two station web cameras provide real time images of the site ,44. The Note: In situ snow depth, SWE, and near-surface air temperature observations from CREST-SAFE will be used in Time domain reflectometry is a highly accurate and automatable method for the determination of the LWC of porous media and its electrical conductivity . LWC is 1/2.Time domain reflectometry is related to the measurement of the relative complex dielectric constant, which is a component of the capacitance . The capThus, the complex dielectric constant of a material can be determined from the propagation of a pulse along a transmission line. The velocity of a pulse vp= The combination of both equations yields:e, Ka=K′ ,25,26,46w~80) and ice (KI~3) is significant at the 1 MHz to 1 GHz range, the dielectric constant is primarily a function of the liquid water content of the snow [A = 1) and ice (KI~3) [In short, a TDR measurement unit consists of a pulse generator , a registration unit  and one or several sets of rods (probes) mounted parallel in the studied medium. The dielectric constant  can be determined from the velocity of propagation of an electromagnetic wave through a medium. The generated pulse is reflected when it reaches the end of the rods. The travel time of the reflected pulse is a function of the dielectric constant of the surrounding medium. Because the difference in dielectric constant between water  . This isThe CS650  is a multi-parameter sensor that uses TDR to measure the liquid water content and electrical conductivity of soils and other porous media. Additionally, it measures the temperature of the medium via a thermistor in contact with one of the rods. It consists of two 30-cm-long (3.2 mm diameter and 3.2 cm spacing) stainless steel rods connected to a printed circuit board. The circuit board is encapsulated in epoxy, and a shielded cable is attached to it for datalogger connection. A five conductor cable including the drain or shield wire is used to provide power and ground as well as serial communication with the CS650. The CS650 is intended to communicate with SDI-12 recorders, including Campbell Scientific dataloggers . The CS6Because the CS650 is a time-domain reflectometer, its fundamental principle is that the velocity of electromagnetic wave propagation along the probe rods is dependent on the dielectric permittivity of the material surrounding the rods, as discussed in The SWP  was builThe operational principle of the sensor is quite simple. When open to the air, the CS650 records the air permittivity, which is one (1). However, when in contact with a porous medium, the instrument records the permittivity (non-one number) of the medium, in this case snow. The instrument was installed on 6 February 2014. At the time, there were around 60 cm of snow in the ground at the station. The results shown in Lastly, the CS650 information is stored on a datalogger (CR 3000) at CREST-SAFE. The CR 3000 was designed for stand-alone operation in harsh, remote environments. The orange Rx wire in the reflectometers can be used to communicate by means of RS-232 Tx/Rx. The A200 USB-to-Serial Module allows RS-232 serial communication between a computer and the CS650 by means of Campbell Scientific’s Device Configuration Utility (DevConfig) software.Campbell Scientific recommends the use of Topp’s equation (Equation (5)) to obtain LWC measurements as a function of the dielectric constant of the medium, as measured by the CS650 probes. Topp et al.  describeTo our knowledge, an equation of similar nature to Topp’s has not been developed for snow due to its heterogeneity and metamorphic changes over time. The studies discussed previously have all focused on developing their own empirical formulas (or using existing formulas) that relate snow dielectric permittivity and density to its LWC. For most mineral soils, Campbell Scientific states that Topp’s equation can be used to obtain LWC estimates using the CS650 dielectric permittivity measurements. However, they suggest a user-derived calibration for other media by describing the relationship between medium permittivity and liquid water content by a quadratic equation or a third order polynomial, much like Topp et al., depending on the number of data points and particular case. Data points should be spaced as evenly as practical over the expected range of LWC and include the wettest and driest expected values. In this paper, Equation (5) was used without any calibration due to the lack of in situ LWC measurements at the CREST-SAFE station. Because of this issue, and the fact that the equation presented by Topp et al.  was devei et al. :(8)Kd = studies ,37,38,42SNTHERM is a freely available, Fortran-written, one-dimensional snowpack physical model that simulates snowpack properties and was first released on 1989. Since its origin, it has been enhanced multiple times to improve its algorithms . SNTHERM3), density limit for compaction of snow (96 kg/m3), and the viscosity coefficient for overburden compaction (6.9 × 105 kg·s/m2)) needed by SNTHERM to simulate the deposition scheme were established based on previous studies [As mentioned in  studies ,66,67,72 studies , whereasIt should be noted that Corona et al.  validateWhile, to our knowledge, SNTHERM LWC simulations have not been validated directly, we understand that the high agreement between SNTHERM and in situ data for other snowpack parameters  shown in previous validation efforts provides sufficient evidence indicating that the SNTHERM LWC simulations  are, in fact, accurate. Firstly, because snow density has been described by snow hydrologists as the ratio between SWE and snow depth , and, more importantly, because—in a simplistic approach—SNTHERM LWC simulations at each node (layer) Thus, accurate SNTHERM LWC simulations will be highly dependent on the already proven accurate snow depth, SWE, and density simulations. However, the mass contribution of liquid water in snow is small, albeit important for hydrology. Hence, good snow density reproduction may be  insufficient to infer that LWC and, equally important, liquid water location within the snowpack are also well reproduced. Instead, these issues might generate uncertainties in SNTHERM LWC simulations. For this reason, possible sources of model uncertainty, as related to the results in It should be mentioned actual SNTHERM node/layer (not bulk) snow LWC values were compared with LWC estimates at single layer depths. The process consisted of finding the cumulative (sum of node/layer thicknesses) snow depth that would lead to the specific SWP sensor height, then the SNTHERM-simulated LWC at that node was extracted and used for comparison with LWC estimates by the three empirical formulas. SNTHERM bulk snow density computations were only discussed because these are part of its density simulation procedure.Note: The irreducible water content for snow is the minimum amount of water that a layer of snow can hold; controlling evaporation and sublimation in the snowpack. Density of new snow is the assumed density for snow precipitation. Density limit for compaction of snow is the upper limit on destructive metamorphism compaction. Lastly, the viscosity coefficient controls the compaction rate of the snowpack due to overburden.SNTHERM, LWCTopp, LWCDenoth, LWCTiuri; where each subscript makes reference to the model or equation these come from, as described in Topp, LWCDenoth, or LWCTiuri, SNTHERM, and Three criteria were used in order to eliminate certain erroneous conclusions that could result from the use of one single evaluation criterion . These wTopp, LWCDenoth, or LWCTiuri and SNTHERM.The MAE is calculated as follows:Topp, LWCDenoth, or LWCTiuri, SNTHERM, While the correlation coefficient R is obtained as follows:The CREST-SAFE Snow Wetness Profiler in situ dielectric permittivity and snowpack temperature measurements at different depths  above the soil surface, along with snow depth and near-surface air temperature data from the station, and SNTHERM melt rate and cold content simulations for winter (6 February–22 April) 2014 are illustrated in As shown in However, LWC (and permittivity) changes can only be partially explained by temperature gradients. Hence, SNTHERM-simulated energy- (cold content) and mass-related (melt rate) snowpack parameters were included in According to y axis)  vs. LWCSNTHERM (x axis) scatter plots for different depths  above the soil surface at CREST-SAFE for winter (6 February–22 April) 2014 are illustrated. SNTHERM and LWCTopp, LWCDenoth, and LWCTiuri at the lower snowpack layers. This is congruent with the melting of the snow layers closer to the snow surface due to daytime solar radiation and warmer air temperatures. Results also positive correlations for all LWC estimates. Moreover, LWCDenoth appears to better capture the LWC changes in the snowpack.The next step was to evaluate whether or not the SWP dielectric permittivity measurements can be translated into accurate LWC estimates. In SNTHERM values were divided into three snowpack categories/conditions: dry, moist, and wet. The idea being to provide insight as to whether or not the high agreement between LWCSNTHERM and LWCTopp, LWCDenoth, and LWCTiuri is mostly due to dry snow conditions. The categories were selected based on a study by Fierz et al. [SNTHERM with LWCTopp, LWCDenoth, and LWCTiuri for different snow conditions. Results indicate that there is higher agreement between LWCSNTHERM and LWCTopp, LWCDenoth, and LWCTiuri whenever the snow is dry or wet, with low agreement for moist snow conditions. This clearly showed some limitations on all three empirical formulas that cannot capture the different behavioural patterns between dry and wet snow. Because the results seem to be influenced by numerous low LWC estimates and the onset of the melt period (LWC > 2%) increases the permittivity and LWC scatter considerably, the LWCz et al. . HoweverSNTHERM (y axis) were plotted against their respective observed SWP in situ dielectric permittivity (x axis) for winter (6 February–22 April) 2014 at CREST-SAFE. After analysing the results obtained in However, because SNTHERM LWC simulations appear to be precise when compared to the LWC estimates obtained using all formulas, this led to the notion that perhaps new statistical relationships between in situ snow dielectric permittivity and LWC simulations can be developed for different snowpack conditions. This way, even if unable to quantify the exact snow LWC, perhaps these new statistical relationships can detect the onset of melt, medium and maximum saturation of the snowpack. In NEW.Henceforth, the LWC obtained using the new statistical relationships presented will be referred to as: LWCThis section intends to tackle on two subjects. The first being the validation of the SWP with CREST-SAFE 2015 data due to the fact that the sensors might have been partially exposed to the air in winter 2014. The second subject is the validation of the new statistical relationships shown in Topp, LWCDenoth, LWCTiuri estimates were obtained and compared with LWCSNTHERM for different depths  above the soil surface at CREST-SAFE for winter (24 November 2014–29 January 2015) 2015. NEW) by comparing them with LWCSNTHERM as well.Dielectric permittivity can be difficult to calculate when the parallel rods are not completely covered by snow. If the rods are exposed to the air, this produces a major (decreasing) effect on the dielectric permittivity of the snow and can increase the error in LWC estimations . While CTopp and LWCDenoth indicate general high agreement with LWCSNTHERM at all snowpack layers, with LWCDenoth showing better overall results again. This might be attributed to the fact that LWCDenoth are not only reliant on dielectric permittivity, but are also snow density dependent. On the other hand, LWCTiuri exhibits significantly high errors. This might be credited to the first assumption the user has to make for dry snow density (200 kg/m3 in this study). Additionally, the dry snow density value used in the equation by Tiuri et al. was different from that calculated by SNTHERM in its subroutine (160 kg/m3). Though the difference is not particularly large, it is still a source of uncertainty when comparing LWCTiuri to LWCSNTHERM. Lastly, while it is known that dry snow density values will vary during a season and between winters, we decided to keep the same value for both winters to avoid additional uncertainties. It should also be noted that Equations (6) and (7) use bulk snow density values. The bulk snow density is the average density of the snowpack , and not the snow density at each specific layer. Although the LWC estimates obtained with the equations by Denoth et al. displayed high agreement with SNTHERM LWC simulations, this could potentially present another source of error. Ideally, the layer snow density would be used in Equation (6) to obtain more accurate LWC estimates. Results display positive correlations for all LWC estimates. It should be mentioned that the CR3000 datalogger to which the SWP is connected stopped working after 29 January 2015 and had to be sent for repairment to its manufacturer. The SWP remained operational, but was not collecting data. From 24 November 2014 to 29 January 2015, the snow depth never reached 60 cm. This explains why only three snowpack layers  are shown in LWCNEW RMSE and MAE values range from 0.64–0.92 and 0.15–0.55, respectively. While R values varied from 0.77 to 0.82. All indicative of high agreement between LWCNEW and LWCSNTHERM and in agreement with previous studies [NEW appears to provide LWC estimates with better accuracy than the other empirical formulas. However, since it is more important to know whether or not the newly developed statistical relationships can estimate LWC accurately for dry and wet snow conditions, the LWCSNTHERM values were divided into three  snowpack categories/conditions again, and the results were compared with LWCTopp, LWCDenoth, LWCTiuri, and LWCNEW. The results  of the snow categorization comparison are shown in SNTHERM and LWCTopp, LWCDenoth, and LWCTiuri whenever the snow is dry or wet, and lower agreement is exhibited for moist snow conditions. All three formulas yielded similar results. Additionally, interannual comparisons do not demonstrate significant changes in agreement between LWCSNTHERM and LWCTopp, LWCDenoth, and LWCTiuri on a yearly basis. Lastly, when comparing LWCSNTHERM with LWCNEW, results indicated improvements over all LWC estimations when using the new statistical relationships on different snow conditions. RMSE and MAE values ranged from 1.95–2.97 and 1.71–2.73, respectively, and R values fluctuated from 0.33 to 0.67. These results demonstrated smaller errors and better correlations when compared to the LWC estimates by all other empirical formulas for all three snowpack states. Furthermore, the LWCNEW estimations displayed a must needed improvement over moist snow conditions; where dielectric permittivity and LWC measurements tend to be less stable.LWC studies ,26,37,38Lastly, a brief (TDR dielectric permittivity) cross-comparison (MAE and relative error (RE)) between the two SWP rods at different snowpack heights above the soil surface is illustrated in SNTHERM dry/moist/wet delimited thresholds (defined previously in this study) using CREST-SAFE 2015 data. It should be noted that the frequency distribution  for the In general, a confusion matrix is a table that is often used to describe the performance of a model (equation or set of equations) on a set of test data  for which the true values are known . In thisPercentages define the percent value of the total number of observations for each class comparison. Green percent values inside the dark grey boxes describe the percentage of correct predictions for that particular row or column. Red percent values inside the dark grey boxes describe the percentage of incorrect predictions for that particular row or column. Results indicate that the new statistical relationships are capable of predicting the snow conditions with an accuracy of 70.7%. The true positive rates  for dry snow and wet snow were 97.8% and 91.8%, respectively. However, as expected, the true positive rate for moist snow was 43.3%. This demonstrates that the new statistical relationships were only correct (when compared to SNTHERM) half the time. However, when it comes to model precision, it is worth mentioning that whenever the new statistical relationships predicted moist snow, these predictions were correct 86.3% of the time. Model precision for dry and wet snow was 66.4% and 65.9%, respectively. These results demonstrate that the SWP was able to capture the changes in snow conditions, even when it was not able to provide exact LWC values.a < 1.5 and 2% < LWC < 5%). Thus, it seems more realistic to use the SWP to detect the onset of melt, medium, and maximum saturation of the snowpack, as demonstrated by the confusion matrix.The SWP is inexpensive, and easy to install and assemble. This experimental setup is capable of detecting changes in LWC continuously and non-destructively over an entire melting period. This means changes in the LWC, such as daily melt-freeze cycles, can be traced with an hourly resolution. While it was not proven that the SWP can produce exact LWC measurements , it was demonstrated that it can recognize changes in the snowpack state . Nonetheless, because the SWP provides the snowpack LWC and temperature profile, is makes it possible for snow scientists to gain insight as to how the water is seeping into the snowpack. However, all three empirical formulas (Equations (5), (7) and (9)) from previous literature ,33,40 anLastly, the SWP can provide valuable information for hydrological applications, e.g., to detect the melt-onset with high temporal resolution. Particularly, if installed in large numbers at different locations; due to their cost efficiency and easy assembly. Sensor networks with large numbers of SWPs could be installed, monitoring on a large scale, e.g., melting processes of an entire hydrological catchment, or on a small scale, e.g., an avalanche prone slope, which is heterogeneously covered by snow.In general, uncertainties in LWC estimates using TDR are dependent on several components. These are mainly snow density variations, temperature influences, and the contact between the probe rods and snow.3 before the snow became wet was held constant over the entire melting period for the sake of the LWC determination using the snow density dependent formulas by Denoth et al. and Tiuri et al., as suggested by Mitterer et al. [3 corresponds to a change in LWC of approximately 1%. A recent study [Aside from the trivial impacts inaccurate snow depth and SWE in situ data might have on snow density values , Variable Infiltration Capacity (VIC) 2-L, National Operational Hydrologic Remote Sensing Center (NOHRSC) snow model (NSM), Lynch-Stieglitz snow model, Snow-Atmosphere-Soil Transfer (SAST), Community Land Model (CLM) snow model, Simplified Simple Biosphere model (SSiB), Noah). First, SNTHERM allows an unlimited number of layers to represent the vertical structure and thermal characteristics of the snowpack, whereas most snow models limit the number of snow layers to three. SNTHERM can therefore potentially resolve more detail in the snowpack profile, and a larger number of thinner layers can allow more accurate solution of vertical fluxes through the snowpack. Secondly, SNTHERM makes forward estimates by subdividing hourly meteorological inputs into smaller time steps, ranging between several seconds and several minutes, until convergence criteria for mass and energy fluxes in each layer have been satisfied. Long-term accuracy is maintained by ensuring the accuracy of each smaller time step. Lastly, SNTHERM uses the temperature and liquid water content of each layer to estimate the thermal conditions of the snowpack. Thus, SNTHERM accounts for the small fraction of liquid water that coexists in equilibrium with snow at temperatures less than 0 °C. However, these advantages  do not make SNTHERM uncertainty-free.3  because snow pit observations at ISW did not support values below this limit. Andreas et al. also reported misestimated heat fluxes in the snow due to errors in thermal conductivity. These errors were also attributed to new snow density. Additionally, SNTHERM, like most other one-dimensional snow models, assumes that the ice matrix and the interstitial air are at the same temperature and that the air is at rest. However, results by Andreas et al. suggested that the simulated near-surface temperatures and the measured snow-surface temperatures are often colder. Mote et al. [Over the years, SNTHERM has been used to simulate snow physical properties, validated with in situ snow observations, and compared with other snow models. Andreas et al.  validatee et al.  comparede et al.  studied e et al.  comparede et al.  carried e et al.  investigUltimately, even though SNTHERM has been proven to be one of the most complex and, consequently, accurate snow physical models, it remains a model nonetheless. As such, it will always remain a conceptualization of real snow physics and has to be treated accordingly. More importantly, the user should always be aware that the model outputs will always be reliant on accurate initial calibration and parametrization; as all study cases are and will be fundamentally different. Lastly, because the movement of liquid water in snow is a complex process, differences between SNTHERM and SWP LWC values might be due to both model errors and instrument inaccuracy.2, operates at 27 MHz, and requires a separate density measurement to solve for the imaginary part of the permittivity. The Finnish Snow Fork samples an area of 6 × 2 cm2, operates at 1 GHz, and simultaneously measures both parts of the medium permittivity. Another commonly-used and -known TDR snow LWC measuring instrument is the Tektronix model 1502C [2, the system operates from DC to 1 GHz and also measures both parts of the medium permittivity. Other less used, yet still relevant TDR snow measurement instruments include: the automatic network analyzer (ANA) type R&S ZPV with tuner unit E3  [2) [When it comes to TDR snow LWC measurements, there are two widely-known instruments: the Denoth meter and Finnish Snow Fork . The Denel 1502C ,26. The 3.5 cm2)  and The cm2) [2) . Additiocm2) [2) . Each ofcm2) [2) . Table 8Topp, LWCDenoth, LWCTiuri, LWCNEW were 2.44, 0.31, 3.70, 0.37 and −0.67, 0.87, −0.53, and −0.79, respectively. As discussed previously, the empirical formula by Denoth et al. seems to provide the more accurate LWC estimates. Whereas the new statistical relationships show a slightly better performance over wet snow conditions. In general, when compared to the study by Stein et al. [In this study, average MAE and R values for LWCn et al. , the resn et al.  reportedn et al.  displayeWe presented an approach to continuously determine snow LWC at different snowpack layers with simple low-cost CS650 reflectometers using TDR. With this experimental setup, it was possible to estimate (with deficiency for moist snow) the snow LWC as a function of its dielectric permittivity. The Snow Wetness Profiler proof of concept demonstrates that it is possible to create an automated, continuous, non-invasive, and non-destructive way of conducting snow LWC and temperature profile observations to further improve our understanding of the interplay between the dielectric permittivity, temperature, and LWC of a snowpack.The accuracy of the SWP for estimating LWC was validated using empirical formulas by Topp et al., Denoth et al., and Tiuri et al. These showed overall good agreement. However, all equations demonstrated an inability to provide accurate LWC estimates for wet snow. Hence, two (for dry and wet snow) new statistical  equations were developed between snow LWC and dielectric permittivity using CREST-SAFE in situ data. The equations displayed a better capability to capture the LWC changes in wet snow. Though, not good enough to be used to provide exact LWC estimates for specific dielectric permittivity values. Thus, at the moment, it seems more realistic to use the SWP to detect the onset of melt, medium, and maximum saturation of the snowpack. Uncertainties such as changes in snow density, snowpack and air temperature, and the possibility of partial sensor exposure to the air remain concerns when dealing with TDR field measurements. Nonetheless, the new statistical equations showed that formulas developed in previous literature have to be reconsidered because most studies provide one general applicable equation for LWC estimates, regardless of snow conditions. Dry and wet snow conditions should be treated separately. Furthermore, those studies that try to consider the differences between dry and wet snow require initial dry snow density or ice content assumptions. Lastly, it should be mentioned that, because these were developed using SNTHERM LWC values, the new statistical equations have to be used with caution. Naturally, it is recommended to use manual in situ LWC measurements and either improve the new statistical equations shown in this study, or develop alternate ones.The main advantages of the SWP are its low cost and low power consumption, and that data analysis is neither time-consuming nor labor intensive. Additionally, the easy assembly makes it an appealing alternative for automated and continuous snow LWC measurements at a high temporal resolution without destroying the snow cover. Moreover, due to the small size of the instruments and the non-destructive measurement setup, these are virtually possible to install anywhere. In light of these preliminary results, the sensors and SWP show promise to become an alternative for in situ continuous and long-term snow LWC monitoring. However, additional work is needed to improve the accuracy of LWC estimations. As mentioned previously, the first recommendation is to use manual in situ LWC measurements to improve the new statistical equations. Secondly, these equations could be further improved if snow density values are incorporated as another independent variable. This was not done in this study because we think it is ideal to provide an equation (or equations) that is only dependent on one snow parameter. In reality, the reflectometers only provide dielectric permittivity values. Hence, if a snow density dependent equation was created, the users will need additional instrumentation to perform those measurements.


The cut-points for weak handgrip strength (kg) for men and women are <26 and <18 in Asians, and <30 and <20 in Caucasians, respectively. The cut-point for slow walking is ≤0.8 m/s in men and women. Current data suggest the potential benefits of physical activity and fitness on sarcopenic obesity in older adults.Sarcopenic obesity is the coexistance of sarcopenia and obesity. Modern sarcopenia definition includes low muscle mass, weak muscle strength (handgrip strength) and poor physical function , although the clinical definition of each varies worldwide. The cut-points for low muscle mass for men and women using appendicular lean mass divided by height (kg/m Sarcopenic obesity (SO) is the combination of low muscle mass (sarcopenia) and obesity. The average prevalence of SO is about 5–10%. Older adults with SO have higher risks of mobility disability, cardiometabolic diseases and mortality. The medical problems related to SO are much greater than that in sarcopenia or obesity alone. Current studies suggest the potential benefits of physical activity, fitness and resistance exercise on the prevention and treatment of SO in older adults. This review highlights the importance of the development of effective public health strategies to prevent, delay and treat SO in older people. Sarcopenia from the Greek ‘sarco’ for flesh and ‘penia’ for loss originally referred to the loss of muscle mass as a natural aging process . In addiCompelling evidence has shown that older adults with SO have higher risks of mobility disability , metabolAlthough a certain degree of muscle loss and fat gain due to aging is inevitable, the good news is that SO is a modifiable condition, thus could be prevented and treated following effective therapy. Among related factors to SO, physical activity has been recognized as a key lifestyle factor to prevent and delay muscle loss and obesity with aging . HoweverIn this review on physical activity and SO, we used PubMed to identify relevant peer-reviewed journal articles (the last search was conducted on 18 December 2015). The search keywords included ‘physical activity’ or ‘exercise’ or ‘fitness’ and ‘sarcopenic obesity’ using the advanced search method. We included all original human studies from cross-sectional surveys, observational cohorts and clinical trials that are not limited by publication year, population age, gender or country. However, conference abstracts, review articles, editorials or commentaries were not included. A total of 96 articles were initially identified. We read each abstract and further reviewed references from selected and eligible articles for additional research. To be included in the quantitative analyses, we required data on risk ratios (odds ratios or relative risk) in observational studies or changes in SO variables in intervention studies, comparing active or exercise groups against inactive or control groups. We found a total of six original research articles that investigated the associations of physical activity, fitness or exercise with SO; five cross-sectional studies and one randomized controlled trial. In addition to study design, the characteristics of the selected studies were further assessed by study population, measurement of physical activity, fitness or exercise program, definitions of sarcopenia and obesity and the prevalence of SO. The data extracted from the selected studies are presented in As mentioned previously, there currently is no universally adopted definition of SO, primarily due to the variations in the definition of sarcopenia . Modern 2), most sarcopenia definitions utilized the ALM index, ALM in kg divided by height squared in meter (kg/m2) [et al. [In recent studies, body composition to measure muscle mass was most commonly acquired from whole body dual-energy x-ray absorptiometry (DXA). For skeletal muscle mass (SMM), appendicular lean mass (ALM) is generally used, which is calculated as the sum of lean mass in arms and legs without including fat and bone mass using DXA. ALM is also referred to as appendicular SMM or appendicular fat free mass. To define sarcopenia, relative muscle mass is typically utilized because absolute muscle mass is highly correlated with height or weight. Similar to body mass index calculation   or divid (kg/m2) , as a ra [et al. . One of  [et al. . Also, b [et al. ,39, bioe [et al.  and ultr [et al. .Handgrip (grip) strength has been widely used for muscle strength measurement, because it is inexpensive, easy to use and well correlated with most relevant health outcomes such as mortality . HandgriThere are a wide range of physical function tests, including usual gait speed, 6 min or 400-m walk tests, timed get-up-and-go test, chair stand test and the short physical performance battery, which is a composite measure of balance, gait and leg strength. However, usual gait speed is the most popular physical function and performance test in clinical practice and sarcopenia research because it is simple, fast and easy to measure as a predictor of mobility limitations and mortality in general populations, as well as patients after cardiac surgery . HabituaBecause sarcopenia no longer refers purely to loss of muscle mass, there are differences in how sarcopenia is defined. 2 for men and 5.67 kg/m2 for women, which is the same cut-point recommended by the International Working Group on Sarcopenia (IWGS) 1 year later [2 for men and <6.42 kg/m2 for women, which were validated by comparing with MRI [2 for men and <6.76 kg/m2 for women, which were developed based on a large US national sample of 4449 older adults [The European Working Group on Sarcopenia in Older People (EWGSOP) recommends using the presence of low muscle mass plus either low muscle strength or poor physical function for the diagnosis of sarcopenia . They noar later . These car later . Also, twith MRI . The othr adults .2 in men and ≤5.67 kg/m2 in women using DXA. These cut-points of ALM index for sarcopenia were developed based on the sex-specific lowest 20% of the distribution of the ALM index from the Health ABC Study (3075 well-functioning men and women aged 70–79 recruited in 1997–1998 from a random sample of Medicare enrollees in Pennsylvania and Tennessee in USA) [In the sarcopenia definition by the IWGS, the diagnosis of sarcopenia was given to individuals with poor physical function plus low muscle mass, without considering handgrip strength . Poor ph in USA) .2 in men and 5.4 kg/m2 in women by using DXA, and 7.0 kg/m2 in men and 5.7 kg/m2 in women by using BIA. However, the cut-points in Asians are lower than those of Caucasian populations using the same DXA, recommended by the EWGSOP and IWGS groups on sarcopenia. This is because of relatively low prevalence of sarcopenia in Asian studies, which is partly due to lower body weight in Asians, compared with Caucasians. There are also differences in socioeconomic factors, lifestyle and culture between Asians and Caucasians. Regarding muscle strength, most Asian sarcopenia research studies used handgrip strength, the AWGS also recommends it as a feasible and convenient measure, similar to other sarcopenia definitions. The cut-point values of low handgrip strength are suggested to be defined as 26 kg in men and 18 kg in women. Regarding physical function, the AWGS recommends using a 6-m usual walking speed. After extensive review of Asian data, slow walking speed was defined as ≤0.8 m/s, which is consistent with the EWGSOP and the US definitions below. However, they reported that there is a potential gender difference in the cut-point value of usual walking speed with a wide range from 0.6 to 1.2 m/s.The Asian Working Group for Sarcopenia (AWGS) supports using both DXA and BIA to define low muscle mass for sarcopenia diagnosis . They in2 in men and 26.9 kg/m2 in women. They recommended sequential screening and case identification processes: screening for physical function first using gait speed, followed by strength assessment using handgrip strength and then assessment of lean mass using DXA. The FNIHSP selected a usual gait speed ≤0.8 m/s for poor physical function, which is the same cut-point suggested by the EWGSOP and the AWGS groups. They reported that 10% of men and 31% of women had gait speed ≤0.8 m/s. The final recommended cut-points for muscle weakness were handgrip strength <26 kg for men and <16 kg for women. Regarding muscle mass assessment, experts in the study preferred a measure that accounts for body mass index instead of height that was suggested earlier in other sarcopenia groups. The final recommended cut-points of ALM/BMI index for low muscle mass using DXA were <0.789 for men and <0.512 for women.In the US Foundation for the National Institutes of Health Sarcopenia Project (FNIHSP), a total of 26,625 participants  were included in the pooled data analyses from nine studies after excluding participants <65 years old . This isBased on a review of the recommendations by four major working groups on sarcopenia above, and a careful examination of the existing literature, we propose the following recommendations .2) and Caucasians (BMI ≥30 mg/m2), suggested by the World Health Organization [2 ≤7.0 kg/m2 in men and ≤5.4 kg/m2 in women using DXA in Asians, as suggested by the Asian group. For Caucasians, ALM/height2 of ≤7.23 kg/m2 in men and ≤5.67 kg/m2 in women using DXA were suggested by European and International groups. However, BMI adjusted ALM cut-points of <0.789 in men and <0.512 in women could also be used in Caucasians, as suggested by the US group. Also, in large population studies when DXA is not available, BIA-based cut-points for low muscle mass shown in First, slow walking  was most commonly defined as gait speed of ≤0.8 m/s in both men and women regardless of race and ethnicity. This cut-point is supported by the European, Asian and the US groups. For muscle strength and mass, it is more appropriate to use separate cut-points for Asians and Caucasians based on higher adiposity in Asians given the same body weight , lower hnization . The recnization , using tA recent study compared sarcopenia prevalence using different definitions based on data from nine studies in an older adults population aged ≥65 years old  [Theoretically, SO has been narrowly defined by low muscle mass and high fat mass, as described earlier in 2 <2.0 SDs [2 using BIA [2 in men and women [2 in men [2 by CT scan [In addition, the cut-point values of each sarcopenia and obesity criterion are also different between studies, depending on population, gender, age, race and ethnicity. Most studies used ALM index divided by height<2.0 SDs ,5,19,56 <2.0 SDs ,20,21,57sing BIA ,23, handsing BIA ,59 or wasing BIA . To defind women ,59 or BM2 in men , and oth2 in men , >28% in2 in men  or the h2 in men ,23. Inst CT scan , ≥90 cm  CT scan ,5,60,61  CT scan ,33,50. A CT scan ,23.Regular physical activity, including both aerobic and resistance exercise, is a significant modifiable factor for the prevention and treatment of obesity in the general population or sarcopenia in older adults. Although physical activity has generally been shown to prevent weight gain and reduce fat mass in people with obesity, while also improving muscle mass and strength in sarcopenic older people, there are still very limited data regarding the benefit of physical activity in individuals with SO. In the Quebec Longitudinal Study involving 904 older adults (mean age: 74 years), the authors investigated the associations between SO and physical fitness using objective measures of body composition and physical fitness in independent older adults . In bothIn the Korean study of 2221 elderly population (mean age: 70 years), investigators examined the associations of exercise and walking with SO . AlthougIn the Spanish study of 306 octogenarians (mean age: 83 years), physical fitness predicted the risk of SO in both men and women . SpecifiWe found only one randomized controlled trial that investigated the effects of a 15-week resistance training on physical function in 17 US older adults aged 60–90 years (mean age: 71 years) with SO at baseline . PhysicaAll six studies suggested potential health benefits of physical activity, fitness and resistance exercise training on SO in older adults in different populations, although the strength of the associations were different between studies. Therefore, the results between studies must be interpreted with caution, and there are several factors affecting the differences among studies such as different methods to measure physical activity, fitness or body composition; different cut-point values to categorize individuals with sarcopenia and/or obesity; age and health conditions of the study populations and different study design. Five out of six studies used a cross-sectional study design, and there is only one small exercise intervention study with a short training period of 15 weeks. Therefore, causality between physical activity and SO cannot be established in most studies, and the effect of changes in physical activity over time on SO remains to be determined. Thus, longitudinal studies are needed to investigate long-term effects of physical activity and fitness on SO in older individuals. Also, more studies are needed to establish the most effective type or combination of exercise and its optimal amount and intensity for the prevention and management of SO. Furthermore, to minimize unfavorable adverse events, exercise prescription should consider the higher risk of injury or complications in the elderly due to clinical conditions such as osteoporosis or cardiometabolic diseases.In addition to physical activity, diet including adequate protein intake and amino acid supplementation is another key factor in effectively enhancing body composition, as recommended for the management of sarcopenia and SO ,18,62,63SO is a multifaceted medical condition with a complicated etiology and results in varying health consequences. The central cause of SO in older adults is the age-related decline in muscle mass and accumulation of fat mass, directly or indirectly through changes in physical activity and diet, as illustrated in Possible reasons for the decline in aerobic and muscle-strengthening physical activity by aging include a decreased occupational activity after retirement, osteoarthritis and muscular skeletal injuries, fear of falling and fracture risk due to osteoporosis and decreased interest and physical ability in sports and exercise. In addition, decreased appetite and gastrointestinal function by aging may also reduce protein intake and digestion, which attributes to muscle loss due to decreased muscle protein synthesis , leadingSO is associated with or leads to several adverse outcomes with increased risks of mobility disability , fallingSO and chronic diseases may also cause physical inactivity due to reduced exercise capacity (decreased cardiorespiratory fitness and muscular strength), physical limitations  and incrCurrent sarcopenia and SO definitions have been primarily developed based on data available from cross-sectional analyses, and there is still no universal definition with consensus. This lack of unified definition for SO has contributed inconsistent findings on the prevalence of SO and the associations between SO and health outcomes. Therefore, it is important to continue to discuss and develop consensus criteria for a clinical definition of SO by comparing different definitions in various populations including those with increased risk of functional disability, bone fracture, joint replacement surgery and other musculoskeletal injuries. It is also important to measure changes in SO variables over time in the same individuals. Therefore, longitudinal studies and randomized controlled trials should be performed to investigate the causation and prognosis of SO. These studies can contribute to finding the effects of changes in ALM, muscle strength, gait speed and fat mass on SO. For clinical trials, including the primary SO parameters  as well as relevant clinical outcomes such as cardiometabolic markers, chronic inflammatory markers, quality of life, falling and fracture, activities of daily living and independence  should be considered to provide evidence based SO prevention and treatment strategies.Based on the significant differences in body composition and muscle strength between men and women, sarcopenia definitions for muscle mass and strength were presented separately by gender. However, regarding gait speed, the same cut-point value is used to define poor physical function, although the value is different between men and women as presented in USA  and the In defining and diagnosing sarcopenia, the current cut-point values for muscle mass were mostly developed based on the lowest 20% or two SDs below the mean value of young healthy reference group from a specific study population. This method is originally designed to parallel the definition of osteoporosis (reduced bone mass), which has been associated with risk of fracture. However, low muscle mass alone is not consistently associated with relevant adverse health outcomes. Therefore, more studies are urgently needed to have reliable and validated reference values for low muscle mass in general populations around the world, as suggested by the FNIHSP . In addiBMI is easy to measure and commonly used to define obesity worldwide as a representative measure of whole body obesity in population levels. However, because BMI does not consider body composition such as muscle and fat mass, its accuracy in research is continuously debated, especially in older adults and specifically in the assessment of SO. Compared with young adults, older adults could have more fat mass given the same BMI levels due to muscle loss by aging. Therefore, it is possible that older adults even in the normal weight category by BMI could have more fat mass, which increases the risk of cardiometabolic diseases such as diabetes or heart attack. Future studies on SO should measure body composition instead of BMI to define SO in older adults. Additionally, because obesity is simply defined by BMI without considering metabolic health and fitness, there are controversial issues regarding different obesity phenotypes such as metabolically healthy obesity , and obeAnother type of muscle disorder that should be studied is dynapenia, which is more closely related to muscle quality rather than muscle quantity (muscle mass). Possible causes of dynapenia include impairments in neural activation, decreased muscle contractile quality and reduced motor unit recruitment , which lThere are also several important questions that should be considered in relation to physical activity and SO. What type and amount of physical activity and exercise are most effective for SO prevention and treatment in older adults with and without functional limitations? What are the practical strategies to promote and increase habitual physical activity in older adults? How can physical activity be combined with other lifestyle factors including sufficient protein intake, healthy diet and chronic disease management for prevention and treatment of SO?Based on the current review, the average prevalence of SO in older adults ranges from 5 to 10% depending on its definitions and study populations, and the prevalence is significantly higher in people aged ≥80 years. The number of older adults aged ≥60 years is expected to more than double, from 841 million in 2013 to over 2 billion in 2050 globally [Sarcopenia and SO studies have emerged only in the past 2–3 decades, and research in this area is still in its infancy. However, the population with SO around the world has major health, social and economic consequences, often with limited coverage of social security system in many developing countries [Although we found many important questions that remain unanswered, it is important to note that SO can be prevented, delayed and treated by maintaining or adopting a healthy lifestyle, including increasing aerobic and muscle-strengthening activities and sufficient protein intake and healthy diet. Specifically, we found potential health benefits of physical activity, fitness and resistance exercise training on SO in older adults in different populations in this review . HoweverSarcopenic obesity (SO) is the combination of low muscle mass plus high fat mass based on the body composition phenotypes. However, modern definition of sarcopenia includes low muscle mass, low muscle strength (weak handgrip strength) and poor physical function .2 are ≤7.0 kg/m2 and ≤5.4 kg/m2 in Asians, and ≤7.23 kg/m2 and ≤5.67 kg/m2 in Caucasians, respectively. The recommended cut-points for weak handgrip strength for men and women are <26 kg and <18 kg in Asians, and <30 kg and <20 kg in Caucasians, respectively. The most common cut-points for slow walking is ≤0.8 m/s in both men and women regardless of race and ethnicity. However, there is a wide range of variations in the definition, assessment and diagnosis of SO for both studies.Based on the sarcopenia definitions suggested by different working groups, the cut-points for low muscle mass for men and women using appendicular lean mass divided by heightAlthough the prevalence of SO varies significantly from 0 to 25% in older adults between studies depending on study populations and the definitions of SO, the average prevalence of SO is about 5–10%, which is similar between men and women. The prevalence of SO is significantly higher in people aged ≥80 years.Older adults with SO have higher risks of mobility disability, metabolic diseases, hypertension, cardiovascular diseases and mortality, which is also related to significantly higher healthcare cost. These clinical problems related to SO is much greater than that in sarcopenia or obesity alone.Despite the limited data, current studies suggest that there are potential health benefits of physical activity, fitness and exercise training on the prevention and treatment of SO in older adults. Sufficient diet such as adequate protein intake and amino acid supplementation is another key factor for the management of SO.Potential mechanisms on the development of SO and its health problems include increased insulin resistance, increased chronic inflammation, decreased hormones, decreased neuromuscular function and decreased energy expenditure and fat oxidation due to aging.Given the rapidly increasing health impact of SO in aging populations in most developed countries, the current review highlights the importance of the urgent investigation and development of universal definition, assessment and diagnosis of SO for both research studies and clinical practice. In addition, we should continue to identify effective public health strategies and programs to prevent, delay and treat SO for millions of older people around the world.


To evaluate the absorption/fluid uptake, solubility and porosity of White mineral trioxide aggregate (MTA) Angelus, Biodentine (BIO), and zinc oxide-eugenol (ZOE).Solubility was evaluated after immersion in distilled water for 7 and 30 days. Porosity was evaluated using digital inverted microscope, scanning electron microscope (SEM) and micro-computed tomography (micro-CT). For the fluid uptake test, specimens were immersed in Hank's balanced salt solution (HBSS) for 1, 7, 14 and 28 days. Fluid absorption, solubility and porosity of the materials were measured after each period. Statistical evaluation was performed using one-way analysis of variance (ANOVA) and Tukey tests, with a significance level at 5%.After 7 and 30 days, BIO showed the highest solubility (p<0.05). All methods demonstrated that MTA had total porosity higher than BIO and ZOE (p<0.05). Micro-CT analysis showed that MTA had the highest porosity at the initial period, after its setting time (p<0.05). After 7 and 30 days, ZOE had porosity lower than MTA and BIO (p<0.05). Absorption was similar among the materials (p>0.05), and higher fluid uptake and solubility were observed for MTA in the fluid uptake test (p<0.05).BIO had the highest solubility in the conventional test and MTA had higher porosity and fluid uptake. ZOE had lower values of solubility, porosity and fluid uptake. Solubility, porosity and fluid uptake are related, and the tests used provided complementary data. It should have low solubility in tissue fluids,,Another important property of root repair cements is the porosity, which can affect the physical properties and, consequently, its behavior within its environmentAs another alternative assay for materials, the fluid uptake test evaluates absorption of fluid , solubility (quantity of substance dissolved in a quantity of solvent) and porosity (empty spaces). This test is not part of the ISO or ANSI/ADA standards, but allows the testing of both solubility and porosity together with absorptionSeveral root repair and root-end filling cements are available for clinicians; and although the clinical performances considering physicochemical and biological properties of these materials have been evaluated, no material can be considered idealThe aim of this study was to evaluate the solubility, porosity, and fluid uptake of root repair cements, using conventional and complementary methods.i.e., fluid uptake) analyses.White MTA Angelus , Biodentine  and zinc oxide-eugenol  were used in the proportions described in The solubility test was performed based on a previous studyThe methodology to evaluate porosity was based on a previous studyPorosity analysis using micro-CT was based on previous study3 and percentage. Reconstruction of the images was performed using NRecon software . The correction parameters for smoothening, beam hardening and ring artefacts were defined for each material . The same parameters were used for the same material at different periods. The reconstructed images were superposed at the different periods and saved in the coronal, sagittal and transaxial planes using the Data Viewer program . The images were analyzed using CTAn software . Open, closed and total porosity were evaluated, in which the closed pores represent empty spaces surrounded by material; and open pores are in contact with the external surface.The samples were examined using micro-CT  after setting  and after immersion in distilled water for the time intervals of 7 and 30 days. The initial porosity of the materials and after contact with the aqueous solution were evaluated. The scanning parameters were: voltage at 80 kV, current at 313 μA, pixel size of 9 μm and 360° rotation using a Cu+Al filter. These images were used for quantitative analysis of the samples, allowing the porosity of the material to be calculated in mmFor analysis of the surface morphology of the different experimental groups, the materials were manipulated and inserted into cylindrical molds with 6 mm in diameter and 12 mm high (n=6). Specimens were kept in an oven at 37°C and immersed in distilled water for 28 days. After this period, the test specimens were dried with absorbent paper and kept in a vacuum desiccator containing silica for 24 hours. The specimens were embedded in resin and polished in an automatic polishing machine . After being dried again, the specimens were placed on stubs, coated with carbon, and examined using a scanning electron microscope  at three magnifications  in secondary backscattered electron mode. All analyses were performed at 18 kV and SS 68. The images obtained were subjected to qualitative evaluation regarding the porosity.This test was based on a previous studyData obtained for all the tests were submitted to normality test, and then to the parametric one-way analysis of variance (ANOVA) statistical test and the Tukey multiple comparison test, with significance level at 5%.At 7 days, BIO showed the highest solubility (p<0.05), followed by ZOE. The value was lower for MTA, which gained weight (p<0.05). After 30 days, MTA and ZOE had comparable solubility (p>0.05), which was lower than that of BIO (p<0.05) .The number of pores was higher for MTA (p<0.05), and there was no statistically significant difference between BIO and ZOE (p>0.05) . RepreseThe porosity observed in micro-CT is represented in Qualitative analysis by scanning electron microscope showed a higher porosity for MTA , becauseMTA had the highest fluid uptake values at all periods (p<0.05), followed by BIO; while lower values were observed for ZOE. There was no statistically significant difference between the absorption for the examined materials (p>0.05). Porosity was higher for MTA and lower for BIO (p<0.05) .,In accordance with the ISO standard 6876/2002, the solubility is evaluated after a period of 24 hours. However, longer periods may be usedThe fluid uptake test also evaluates the solubility of materials. However, when compared to the solubility test, there are differences with the diameter of the specimens, time and immersion solution, and dehydration after removal from the solution. The fluid uptake test allows the analysis of the absorption of fluid, solubility, and porosity of the materials in a single test. In general, this study showed higher values of fluid uptake for MTA, followed by BIO, and lower values for ZOE, without difference in absorption for the tested materials. Regarding the solubility, in the fluid uptake test, the materials were immersed in the HBSS solution for 28 days, whereas the conventional test analyses were performed after immersion in distilled water for 7 and 30 days. MTA showed the greatest increase in solubility after 30 days, suggesting association with the fluid uptake test, in which the highest solubility was observed for MTA after 28 days. Kaup, et al.,A previous investigation of the fluid uptake of three cements  and a calcium silicate-based experimental cementPorosity occurs because of spaces in the non-hydrated cement,The cutting blade used to prepare specimens for surface analysis of materials via SEM may change the porosities, which can influence on the measurement of the number and size of poresAccording to the manufacturer , the CTAn software allows the porosity analysis of any material. In this study, the specimens were scanned in a high pixel size (9 μm), which increased the quality and reliability of images.,Solubility and porosity have been linked to each other. The solubility observed for MTA may be associated with the presence of bismuth oxide as radiopacifier, which increases the porosity of the cement, decreasing its mechanical stability and increasing its solubility,MTA had greater fluid absorption in the fluid uptake test, which may favor the expansion of the material. However, absorption may favor an increase in porosityBIO has a reduced particle sizeBIO showed the highest solubility in conventional test. MTA had higher fluid uptake and total porosity values when evaluated by microscope, SEM, fluid uptake test and micro-CT in the initial period. ZOE had lower values of solubility, porosity and fluid uptake. Solubility, porosity and fluid uptake are related, and the conventional and modern tests provided complementary information and results.


The symposium on visualization in high-performance computing (VHPC) was held in conjunction with ACM SIGGRAPH Asia 2015 in Kobe, Japan, between 2 and 4 November, 2015. The number of participants was over 260 from Japan and a few other countries. The state-of-the-arts in many aspects of scientific visualization, information visualization and visual analytics were presented and discussed. In total, 45 papers were submitted from five countries. After a rigorous peer review process where each paper was reviewed by at least two reviewers, 15 full papers were selected for presentation. Three speakers were invited by VHPC. As a keynote presentation, Prof. Kwan-Liu Ma from the University of California, Davis (UC Davis), USA, gave a talk titled “Visualization and high performance computing”. Dr. E. Wes Bethel from Lawrence Berkeley National Laboratory (LBNL), USA, presented his talk titled “HPC visualization and analysis at the exascale: big headaches, big opportunities” as an invited talk. Dr. Hirofumi Seo from SCIEMENT Inc., Japan, gave a presentation of his visualization works in medical front as a special talk. In addition, VHPC featured two tutorials and a visualization contest. In the final session, VHPC also featured a panel discussion, including six panelists on the unsolved visualization research problems.Journal of Visualization (JOV). We wish to thank the editors and reviewers of JOV for making it possible to publish this special feature from VHPC. We also thank the authors for their careful and insightful work and cooperation in the preparation of the revised papers. It will be our pleasure if readers appreciate the hot topics in visualization research as a result of this special feature. We would like to express our sincere thanks to the staff at Springer-Verlag for their kind support.In the symposium, 15 papers were selected for presentation from 45 submissions. Among those presentations, four papers were finally accepted for publication by the standard peer review process of


The common cold (CC) is the most common syndromes of infection in human beings, but there is currently no special treatment. For this reason, acupuncture is used to relieve the symptoms of the CC. Acupuncture is a traditional Chinese medicine (TCM) therapy that has been used for over 2000 years to treat various diseases. However, few studies have provided evidence for the efficacy and safety of acupuncture for the CC. This study aims to evaluate the effectiveness and safety of acupuncture on CC periods and its symptoms.The following electronic databases will be searched for studies conducted through January 1, 2019: Web of Science, Cochrane Library, EBASE, World Health Organization International Clinical Trials Registry Platform, Springer, Wan-fang database, Chinese Biomedical Literature Database (CBM), Chinese Scientific Journal Database (VIP), China National Knowledge Infrastructure (CNKI), and other sources. All randomized controlled trials on acupuncture for common cold will be included. Risk of bias will be assessed using the Cochrane risk of bias assessment tool, while RevMan V.5.3.5 software will be implemented for the assessment of bias risk, data synthesis, subgroup analysis, and meta-analyses if conditions are met. Continuous outcomes will be presented as mean difference (MD) or standard mean difference (SMD), while dichotomous data will be expressed as relative risk.A high-quality synthesis of current evidence of acupuncture for CC will be stated from several aspect using subjective reports and objective measures of performance. The reduction rate of common cold symptoms after initial treatment, resolved cold symptoms, and reduced cold duration will be collected.This protocol will present the evidence of whether acupuncture therapy is an effective intervention for CC. It is a spontaneously remitting infection of the upper respiratory tract, characterized by a runny nose, nasal obstruction, headache, sneezing, cough, and fever . There is uncertainty regarding the efficacy and safety of interventions for preventing and treating the common cold in healthy people because the frequent alterations in the antigenic structures of respiratory viruses. Further, even bacteria act as infective agents.Respiratory viral infections, also known as the common cold (CC), are the ubiquitous syndrome of infection in human beings. from 30% to 50% of cases of cold syndrome are caused by rhinoviruses, but the etiology can vary depending on the epidemiological situation. Bacterial infections were rare, suggesting that the common cold is almost exclusively a viral infection. So far our understanding of the mechanisms that generate cold symptoms is limited. Today, conventional methods of medications, such as analgesic agents and antihistamines are only effective for the alleviation of symptoms, and antibiotic treatment is not justified.Accordingly to the research,Acupuncture is prevalent in South Asia like China, Korea, and Japan. The main theories are based on Yin-Yang, Qi, meridian and acupoints. Each acupoint has its own specific therapeutic functions, and the prescription of acupoints is formulated by different acupoints and special stimuli ways based on ancient Chinese Medicine theory. In 1997, a National Institutes of Health Consensus Development Panel on acupuncture reached the conclusion that there is clear evidence that needle acupuncture is efficacious for many diseases, particularly in relief of pain on diverse pain conditions.Since the early 1970s, acupuncture began to spread in West, and almost 500 randomized controlled trials (RCTs) have evaluated its efficacy. impair leukocyte adherence to vascular endothelium, and suppress exudative reaction a degree equivalent to that of orally administered aspirin and indomethacin. Evidence also supports that there was an adjusting-network of immune in the body, and acupuncture therapy can heighten the cellular immune function of patients and provide a beneficial effect by affecting the levels of T lymphocytes subgroup , soluble interleukin-2 receptor (SIL-2R), and beta-endorphin (beta-EP) in the peripheral blood of patients. It was also reported that acupuncture has detoxification effect through the amelioration of antioxidant defense systems.The effect mechanism of common cold by acupuncture has not been fully found so far. However, accumulated evidences have shown acupuncture may inhibit early-phase vascular permeability,The research will summarize the acupuncture therapy methods as intervention for common cold, to evaluate the therapeutic value of acupuncture and provide clinical decision scheme for clinicians.2This systematic review protocol has been registered in the PROSPERO international prospective register of systematic reviews (CRD 42018085862).2.12.1.1RCT and blinded related research will be included only. All original studies in English or Chinese that reported the effectiveness and/or safety on acupuncture for CC will be included. We will examine RCTs that involve at least 1 test treatment that aimed to improve or eliminate CC symptoms, and 1 control treatment (or no treatment) with concurrent enrolment.2.1.2Study participants diagnosed with CC must conform to diagnosis standards established by bodies such as the American Academy of Physician Pediatrics Committee or American Academy of Physician Assistant. Many other diagnostic distinctions in patients with a common cold or similar acute respiratory illnesses can be made, using readily available information. There are no limits on the ethnicity, nationality, type of CC, or sex of the subjects; patients in different age ranges will be included in the study.2.1.3Several methods of acup will be included in the review: fire needle therapy, fine needles, electric needle therapy, floating needle therapy, acupoint application, and acupoint injection. We will also include trials that evaluate acupuncture as a combination to other therapies. Multiple control interventions will also be included: no treatment, placebo, and other interventions .2.1.4Primary outcomes will include: the change in severity of overall symptoms of the common cold ; the change in duration of overall symptoms of the common cold .Secondary outcomes will include: the change in severity of individual symptoms ; the change in duration of individual symptoms ; side effects from using acupuncture.2.22.2.1Relevant databases include: Medline, Cochrane Library, Web of Science, EBASE, Springer, WHO International Clinical Trials Registry Platform (ICTRP), China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedical Literature Database (CBM), Chinese Scientific Journal Database (VIP). All RCTs published in electronic databases from inception to January 1, 2019 with language restricted in Chinses and English will be included in this review study. The Medline search strategy is listed in Table 2.32.3.1Depending on the type of literature research, intervention measures, and object of study, we will rule out unrelated literatures in early screening. The study of screening flow diagram is summarized as Fig. 2.3.2 Two RCTs compared the effectiveness of acupuncture with a control. In addition, controversial problems will be resolved by consulting experts. Study quality will assessed independently by 2 investigators (YC and BG) including what randomized method has been used, whether the results of the data are complete and whether grouping scheme is hidden or not.Investigators will evaluate quality of articles and assess the risk of bias based on the domains and criteria of the Cochrane Collaboration tool .2.3.3Data will be extracted independently by 2 investigators (YJ and NX) using a data extraction sheet. The datum of each selected trial will be collected and recorded as a electronic form which includes that basic information of studies , sample size and grouping method; details of intervention methods include treatment time, selection of therapy, acupoints, data type of treatment efficacy, treatment cycles, ascertainment of outcome, side effects, and follow-up.2.3.4A risk ratio (RR) with 95% confidence intervals (CIs) is used to estimate the overall outcome of treatment with dichotomous data. A standard mean difference (MD) or standard MD (SMD) with 95% CI will be presented for continuous outcomes. Other binary data will be altered into the RR form. This work will be performed by 2 independent persons to reduce inaccuracy in the extracted rates.2.3.5In view of the lack of data that needs to be extracted in the included document, if there is a statistical missing data, we will attempt to contact the authors by phone or email. Articles in which data are not given or the given information could not be computed will be excluded.2.3.6P values, and the heterogeneity will be evaluated by I2 statistic. An interpretation of I2is as follows: I2 ≥50% will be considered a measure of severe heterogeneity, while I2 <50% will be presented as no heterogeneity. So the conclusion will be made with discretion. If the clinical and methodological heterogeneity are not presented, the stochastic effect model will be assessed by merger analysis. If the heterogeneity is oversized, descriptive analysis will be performed.To assess heterogeneity among the studies, we will use chi-squared test and 2.3.7Funnel plots and statistic test will be used to detect reporting bias on overall estimate.2.3.8I2 < 50%, the fixed effect model will be selected and the random-effect model will be selected I2 ≥50%. Otherwise, we will exclude study from meta-analysis.Based on the sample size of the clinical research including the research designing of measurement methods, intervention methods, and the length of treatment, we will perform the systematic review of the literature with meta-analysis. When multiple homogeneity studies are included, meta-analyses will be performed with Review Manager 5.3.5. When 2.3.9(1)Different types of acupuncture therapies.(2)Symptoms of CC .If the heterogeneity is caused by clinical trials above-mentioned, subgroup analysis will be conducted, and detailed subgroup will be classified. If we identify substantial heterogeneity according to the outcomes of data synthesis, the following subgroup analyses plan to carry out.Sensitivity analysis: By changing the genre of research (incorporating or excluding a particular study), reanalysis of simulated missing data we will observe fluctuation of termination.3CC comprises symptoms of headache, sneezing, soar throat, fever, nasal congestion, cough, and general pain with anhidrosis. traditional Chinese acupuncture shown to be effective and safe with strong operability, low cost, and environmental friendly, especially suitable for children with fever. Acupuncture is based on the theory of TCM guided by the theory of meridians. A present review suggests that a significantly positive benefit is obtained from the administration of acupuncture therapy. The use of acupuncture for symptoms of the common cold symptoms should be considered, although further evidence from placebo controlled RCTs is required.Having the effect of promoting metabolism circulation, balancing, improving immunity, dredging meridian, and harmonizing qi and blood,According to Cochrane method, this research is based on the existing clinical RCT evidence analysis at home and abroad, retrieving and screening the main electronic literature database with evidence of evidence-based medicine, to better guide clinical practice.


This prospective longitudinal study investigated the effects of a dietary supplement in patients affected by reduced sperm motility  with metabolic syndrome. The product tested was Andrositol®, which contains myoinositol (MI) as principal compound, in association with other molecules, and the parameters evaluated were semen characteristics as well as hormone and metabolic profiles. The inclusion criteria were subjects aged over 18 years, with asthenospermia and metabolic syndrome. The exclusion criteria were presence of cryptorchidism, varicocele, and prostatitis. For this study, 45 males who had such features were enrolled. Their selection was made according to the 2010 World Health Organization (WHO) criteria (5th Edition) for the Evaluation of Human Semen. Hormone and metabolic profiles and semen parameters were assessed at the beginning of the study and after three months of treatment with Andrositol. The differences between the values before and after the supplementation were found statistically significant. Andrositol normalized the metabolic profile of these patients, improving their insulin sensitivity. Moreover, testosterone levels were increased and the semen characteristics, such as sperm concentration, motility, and morphology, highly improved. In conclusion, the association of MI with other molecules (micronutrients and vitamins) could be an effective therapy for metabolic disorders, as well as hormonal and spermatic changes responsible for male infertility. Myoinositol (MI) is a sugar-like molecule and it is one of the precursors for the synthesis of phosphatidylinositol polyphosphates (PIPs), key biomolecules belonging to the signal transduction system of several cellular functions . MI exer in vitro [Recently, a bidirectional interaction between MS and asthenospermia has been proved . In asthin vitro , 22 in oThe present study attempts to determine, for the first time, the effects of a dietary supplement containing MI, selenium, and L-arginine in asthenospermic patients affected by MS. The authors evaluated the effects of this administration on semen parameters, as well as on hormone and metabolic profiles.This is a prospective longitudinal study of asthenospermic males with MS, under treatment at Altamedica IVF Unit, Rome, Italy. All participants have signed an informed consent form. The inclusion criteria were subjects aged over 18 years, with asthenospermia and MS. The exclusion criteria were presence of cryptorchidism, varicocele, and prostatitis. Overall, 45 males were enrolled from January to April, 2011. Asthenospermia was defined according to the 2010 World Health Organization criteria (5th Edition) for the Evaluation of Human Semen. The following parameters, established by the National Cholesterol Education Program, were used to assess whether the subjects were affected by MS: fasting plasma glucose ≥ 100 mg/dL or diagnosis of diabetes; waist circumference > 102 cm in males; blood pressure ≥ 130/85 mm Hg; triglycerides ≥ 150 mg/dL; HDL cholesterol < 40 mg/dL in males.The medical histories of all patients were taken into consideration and physical examinations were conducted by the same physicians.μg selenium, and 200 μg folic acid .Hormone and metabolic profiles as well as semen parameters were evaluated in the relevant study at the beginning and after three months of therapy. The patients were treated by a dietary supplement administered twice a day containing 1 g MI, 30 mg L-carnitine, L-arginine and vitamin E, 55 In order to determinate metabolic and hormonal profiles, the semen and blood samples were obtained from all patients before and at the end of treatment. The semen samples, obtained by masturbation after 3 to 5 days of sexual abstinence, were analysed immediately after complete liquefaction. Each patient was asked to provide three samples, taken up in different days, with the purpose to reduce the variability due to the use of drugs and alcohol or the presence of fever in the days before the test.Sperm features were evaluated by the same examiner, according to the World Health Organization guidelines .The homeostasis model for insulin resistance (HOMA) index was calculated as the product of the fasting plasma insulin (mIU/mL) and glucose (mmol/L) concentrations divided by 22.5 . Waist cPlasma luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol (E2), sex hormone-binding globulin (SHBG), free testosterone, and testosterone (T) levels were measured by radioimmunoassay (RIA). These hormones may contribute to changing sperm concentration, total motility, and morphology , 25.t-test. P < 0.05 was considered statistically significant. GraphPad Prism software  was employed for the data analysis.The results are presented as mean ± standard deviation. The differences in variables before and after MI supplementation were statistically analysed with Student's paired P < 0.001).P < 0.01 and P < 0.001, resp.). Following the treatment, authors observed that a statistically significant increase in LH levels (P < 0.01), as well as in free (P < 0.001) and total testosterone levels (P < 0.02), occurred. In regard of hormone profile, only FSH levels were not affected by the supplementation, whilst plasma, E2, and SHBG levels decreased significantly after the treatment .After the treatment, all sperm characteristics were significantly improved (Herein, the authors have determined the effects of a treatment for three months by using a dietary supplement containing MI, in association with other micronutrients and vitamins on metabolic and hormone profiles and on semen parameters of asthenospermic patients with MS.MS is a combination of disorders, including obesity, that can increase the risk of developing cardiovascular disease and diabetes , 27. It Insulin resistance is likely to be responsible, along with adipose tissue mediated signs, for the regulation of gonadal function, causing changes in hormone and proinflammatory cytokines levels , 29–31.Male infertility is frequently due to asthenospermia which ensues a reduction in sperm motility .Furthermore, this condition is generally associated with decreased T, plus elevated E2, FSH, and LH levels . in vivo study that focuses on this topic. Nevertheless, several evidences of MI clinical efficacy in women with PCOS [To date, this is the firstith PCOS –35 and iith PCOS , 14.The administration of this dietary supplement normalized the metabolic profile of asthenospermic patients with MS, increasing their insulin sensitivity without significant changes in BMI, WC, and triglycerides levels. Moreover, in these subjects, the treatment increased T levels and significantly improved semen characteristics.It is noteworthy that reduced T levels have been associated with MS and particularly obesity . We may A replacement therapy with T may improve this condition in these patients; however, it is not completely effective for the treatment of infertility in men , 43.Therefore, new drugs and molecules that increase insulin sensitivity, raising T levels at the same time, are desirable.3 and calcium channels opening leading to an increase in Ca2+ intracellular concentrations in the flagellum [Recent studies  suggestelagellum . A recenlagellum .Our clinical study has demonstrated its effectiveness for MS as well as ameliorating the hormonal and spermatic conditions which imply male infertility. As shown, patients with asthenospermia and MS definitely improved their condition after MI use.The association of selenium and arginine with MI appears particularly interesting. In fact, selenium is a micronutrient important for the male gonad and the process of male reproduction , 48. Thi in vitro with humans, rabbits, and goats [Also a deficiency in L-arginine content is harmful for male fertility since this amino acid is strongly involved in the process of sperm formation . Reducednd goats –57. In pnd goats , 59.Moreover, L-arginine is essential to relieve constricted arteries due to its role in generating a mediator called nitric oxide. It was shown that the administration of this amino acid helps the artery dilatation, increasing blood volume .Overall, these clinical results lead us to deem that the success of the therapy with Andrositol could be mainly due to the association of MI with selenium and L-arginine. It is likely that the antioxidant role of these last two molecules has been important for the improvement of sperm parameters. On the other hand, MI may have helped to balance the hormonal and metabolic parameters, as it acts as second messenger regulating the activities of several hormones such as FSH, TSH, and insulin . In concThere are several limitations to the present study, such as lack of controls, limited number of patients, brief treatment period, and unsegmented group of subjects with MS. Therefore, in follow-up, larger prospective randomized case-control studies are needed to elucidate the role and effects exerted by MI, selenium, and L-arginine in asthenospermic patients with different clinical presentations of MS.