We identified few substances selectively inhibiting only the α′ subunit. Other compounds showed similar effect towards all four isoforms. In some cases, like chrysoeriol, pedalitin, apigenin, and luteolin, the α2β2 holoenzyme was at least six times better inhibited than the free α subunit. Otherwise, we have found a luteolin derivative decreased the kinase activity of CK2α′ with an IC50 value of 0.8 μM, but the holoenzyme only with 9.5 µM.CK2 is a pleiotropic, constitutively active protein kinase responsible for the phosphorylation of more than 300 physiological substrates. Typically, this enzyme is found in tetrameric form consisting of two regulatory subunits CK2β and two catalytic subunits CK2α or CK2α′. Several natural occurring flavonoids were tested for their ability to inhibit both CK2 holoenzymes, CK2α Protein kinase CK2 is a highly conserved serine/threonine kinase considered as constitutively active and ubiquitously expressed. CK2 is known to play an essential role in cellular processes, e.g., cell proliferation, cell growth, and cell survival, cell morphology, and promotes angiogenesis –9. In maCK2 can function as monomeric kinases, but also as tetrameric complexes. In human, the monomeric forms are designated as CK2α and CK2α′, being the catalytic subunits of CK2. The tetrameric isoforms consist of two catalytic subunits each bound to a regulatory subunit CK2β whereas two of them form a dimer. Within the CK2 holoenzyme, the regulatory CK2 subunit alters substrate specificity . Calmodu46XGXXS51), the catalytic loop , the activation loop (sequences D175WG177 and G199PE201), and the substrate binding site  indole derivatives, pyrazolo-triazines, and carboxyl acid derivatives ATP (specific radioactivity 500–1000 cpm/pmol) in the presence of 10 μM yeast P2B as substrate. Incubation was performed at 37 °C for 15 min. Afterwards, the reaction was terminated by adding 7 μL of SDS-PAGE loading buffer. Reaction mixtures were resolved in SDS-PAGE followed by Coomassie blue staining and autoradiography. The phosphate incorporation level in the P2B protein was estimated by cutting off the corresponding band and measuring the radioactivity in a scintillation counter (Perkin-Elmer).CK2 activity was determined in a standard reaction mixture  containing 20 mM Tris–HCl buffer, pH 7.5, 15 mM MgClPhosphorylation of the peptide substrate (50 μM) was terminated by the addition of 10% orthophosphoric acid, and aliquots were spotted onto phosphocellulose filters (Whatmann P81). Filters were washed with 1% orthophosphoric acid three times and dried before counting in scintillation counter.http://www.swissdock.ch/) web service developed by the Molecular Modeling group of the Swiss Institute of Bioinformatics . The modeled and docked structures were analyzed with UCSF Chimera 1.12rc software. The binding site of the structures was identified using the crystal structure of CK2α (PDB code 1PJK), CK2α′ (PDB code 3OFM), and holoenzyme CK2α2β2 (1JWH). The docked structures showed binding energy in the range of − 7.49 to − 8.48 kcal/mol.CK2 protein structures were modeled and ligand binding studies were carried out using SwissDock , luteolin (5), kaempferol (7), and quercetin (8) were already tested on human CK2α2β2 and human CK2α′2β2 [E. coli, mixing bacteria pellets expressing GST-CK2α and His-CK2β as well as GST-CK2α′ and His-CK2β and purified to homogeneity by affinity chromatography using glutathione-sepharose . The inhibitory effect was examined by increasing concentrations of the compound. The experiments were conducted using either P2B or the synthetic peptide as phosphoacceptor. Our obtained results are summarized in Table 1), pedalitin (2), apigenin (4), luteolin (5), kaempferol (7), quercetin (8), and cernuoside (10) were tested towards both holoenzymes using P2B as substrate. A comparison of chrysoeriol (1) and pedalitin (2) in Fig. 50 values were lower using CK2α2β2 than for the CK2α′2β2 holoenzyme. In case of free catalytic subunits, the inhibitory effect was almost always stronger towards the CK2α′ subunit. Comparing the inhibition of the free CK2α′ subunit with IC50 values from 0.06 to 5.1 μM for chrysoeriol and cernuoside, respectively, with its holoenzyme (0.1–7.8 μM), the IC50 values were similar or higher for the holoenzyme. The difference between CK2α and the holoenzyme was significant, with IC50 values testing the free catalytic subunit of 0.42 μM for chrysoeriol and 9.8 μM for apigenin, but 0.06 μM for chrysoeriol and 1.5 μM for apigenin in case of the holoenzyme. Most potent inhibitor as in case for the free catalytic subunits is chrysoeriol (1) inhibiting all four molecular forms of CK2 with IC50 values below 0.42 μM. Interestingly, luteolin derivatives  revealed opposite effect on CK2 than the parent compound. Whereas the inhibition is stronger comparing the holoenzyme and the respective free catalytic subunit (6.0 vs. 0.55 μM for CK2α and holoenzyme) in the presence of luteolin (5), both derivatives possess similar or even weaker activities towards the holoenzymes. Also, the strong inhibitory potential was lost in case of both luteolin derivatives  on the CK2α′2β2 holoenzyme compared to luteolin (5) which was not the case for the free catalytic subunits. This decrease was even much stronger for the CK2α′2β2 holoenzyme with IC50 values about tenfold higher. Pedalitin (2), tricin (3), luteolin (5), kaempferol (7) are the only compounds inhibiting both holoenzymes better than the respective free catalytic subunits. Within the tested compounds, we found examples where inhibition of the holoenzyme was detected even in case where no effect was seen for the free catalytic subunit. Isokaempferide (6) influenced the CK2 activity of both holoenzymes with IC50 values of 17.5 and 12.1 μM. Its derivatives (6a–c) showed different effects with IC50 of 9.9 and 3.5 μM (compound 6b) or without any inhibition . Otherwise, the same derivatives of apigenin (4), namely the 7-O-glucoside (4a), 7-O-glucuronide (4b), and 7-O-methylglucuronide (4c) inhibited with a similar extent  both holoenzymes but much less effective than the parent compound apigenin (4) inhibiting the CK2 activities to 50% at 1.5 and 2.5 μM.In previous studies, we had described the inhibitory effect of different substances like halogenated nd CK2α′ , 23. In CK2α′2β2 , 22. Add4a), luteolin (5a), isokaempferide (6a), kaempferol (7a), and quercetin (8a). The decrease in the potential of these compounds lies between sixfold up to over 100-fold for apigenin 7-O-glucoside and quercetin 3-O-glucoside, respectively. Noteworthy compounds 6a and 7a did not show any effect on the CK2 activities.The glucosylation at position C-3 or C-7 dramatically lowered the inhibitory potency in case of derivatives of apigenin (9) was shown to exhibit very weak potential towards CK2α but good inhibition in case of CK2α′. Similar effect was detected for scutellarein 7-O-glucoside (9a) which completely failed in case of CK2α. Interestingly, compound 9a possesses very good inhibitory potential towards CK2α2β2 with an IC50 of 0.4 µM, but weaker potential towards CK2α′2β2 than on CK2α′  and cernuoside (10) possess similar inhibitory potential towards CK2α and its holoenzyme. Apigenin 7-O-glucuronide (4b) and luteolin 7-O-glucoside (5a) were less active on CK2α2β2 than towards CK2α but still had a moderate inhibitory effect with IC50 values of 9.4 and 9.0 µM. The O-glucoside derivatives of isokaempferide (6a), kaempferol (7a), and quercetin (8a) as well as isokaempferide 7-O-methylglucuronide (6c) were completely ineffective towards both holoenzymes similar like towards the free catalytic subunit with exception of 6c and quercetin 3-O-glucoside (8a) with IC50 values of 17 and 3.7 µM towards CK2α, respectively (Table O-glucoside (4a) showed similar inhibitory effect towards both holoenzymes, whereas it was inactive on free catalytic subunits. There are several compounds inhibiting CK2α′ and its holoenzyme in the same range, like pedalitin (2), tricin (3), apigenin (4), luteolin (5), isokaempferide (6), kaempferol (7). Other compounds, like chrysoeriol (1), quercetin (8), scutellarin (9), and cernuoside (10) showed a 2.5-fold stronger influence on CK2α′ than on the derived holoenzyme , apigenin (4), and quercetin (8) with the three available CK2 isoform structures  instead of the typical tetrameric form [The CK2 holoenzymes differ in their biochemical characteristics which could be the reason for various sensitivity towards inhibitors. The study of Olsen et al. described the similarities and differences between the CK2α- and CK2α′-derived holoenzymes . Interesric form .2β2, CK2α′2β2, whereas, the holoenzyme is the predominantly occurring form in the cell.Protein kinase CK2 exists as several isoforms, namely CK2α, CK2α′, CK2α2β2 holoenzyme.As reported in several publications, the CK2β subunit regulates the preference for the protein substrate and possesses influence towards the structure of the ATP/GTP-binding site , 38, 39.Flavonoids are promising compounds in the search for new CK2 inhibitors and in further future as potential anti-cancer drugs. There are numerous reports about their cytotoxic effect on cancer cell lines in vitro or chemopreventive possibilities as components of daily diet .


In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R A wide variety of cheese and cheese products can be purchased in stores around the world. Cheese is an important source of nutrients, such as fat and protein , and is That is why, fast and non-destructive spectral methods were developed to measure main components, such as fat, protein and moisture, of cheese ,7,8,9. MNIRS is widely applied in food analyses ,18,19,20But the question of the performance of those pocket-sized spectrometers remains. Since they are not targeted at scientists and researchers, there is little knowledge about the performance of these spectrometers. This is why the present study aims at shedding light upon some aspects of the performance of a pocket-sized molecular sensor—The SCiO . Only few studies have been published using this device and thus more information is needed ,32.SCiO is operated with a smartphone via Bluetooth. SCiO can be controlled using either the SCiO or the SCiO Lab app. The SCiO app contains a set of pre-established calibration models for, for example, calories and water content of fruits and vegetables; and sugar, fat and calories for chocolates. It also contains a pre-established app for water, protein and fat content of dairy products like cheese, yoghurts and puddings. Hence, once a sample is measured all values are directly given to the user, without presenting a spectrum or a model. The main advantage of this app is its easy handling and intuitiveness. A disadvantage of the SCiO app is that only products for which a calibration model already exists can be measured. If the user wants to measure another product, the SCiO Lab app has to be used. There, spectra can be recorded and later be analysed using the cloud-based web application, SCiO Lab.2).The current study investigates different cheese samples in terms of their fat and moisture content using a benchtop NIR spectrometer, as well as SCiO. The performance of both devices is compared using statistical parameters, such as the standard error of prediction and the coefficient of determination (R−1) and is also called the “Herschel” region. It is the only region where electronic transitions can be observed. Furthermore, the Herschel region contains overtones and combination bands. Region II is located from 1200–1800 nm (8500–5500 cm−1) and mainly comprises first overtones. Region III ranges from 1800–2500 nm (5500–4000 cm−1), where the combination band can be found [The NIR region is often divided into three sub-regions: Region I ranges from 800–1200 nm (12500–8500 cmbe found .−1) and thus it mainly includes vibrations in region II and III. The benchtop device also reaches into the Herschel region; however, it does not cover it completely. SCiO on the other hand exclusively records spectra in region I, as its wavelength range reaches from 740–1070 nm (13514–9346 cm−1). This also means that SCiO reaches into the visible part of the electromagnetic spectrum. This makes a comparison between the two devices interesting, as the recorded vibrations influence the performance of each spectrometer. The NIR region was chosen as the reference range for various reasons. First of all, molecular vibrations corresponding to water and fat are quite pronounced in this region. Secondly, the different colours of the cheese are irrelevant in this region. Thirdly, using SCiO and the NIRFlex N-500, the same measurement setup and mode could be used. As shown in First, all models developed using The Unscrambler X version 10.5 are presented and evaluated. Then the data generated with the SCiO web-application will be examined in detail and, lastly, the performance of the pre-established models from the SCiO app will be discussed.For spectral data pre-treatment, the descriptive statistics tool, which is implemented in The Unscrambler X, was consulted and then a fitting pre-treatment was applied see . Table 2For whole pieces of cheese and grated cheese measured with the NIRFlex N-500 and SCiO, two principle components (PCs) were needed to develop calibration models for fat content. Cheese has a high fat content, which is why a low number of principle components for calibration models can be expected. The number of PCs used was determined using the variance plot, which showed how much of the original information is included in the respective PC. Since the same number of PCs were used for calibrations established with SCiO and the benchtop device, as well as for grated and whole pieces of cheese, the results had a high comparability.The coefficients of determination for all models were between 0.9431 and 0.9940. This indicated a high correlation between the spectra and the reference data. The errors of the cross-validated calibration models, called root mean square errors of cross validation (RMSECV), were between 0.78% and 1.57%, which was acceptable, considering that the average of the reference data was 29.17%. When investigating the test set-validated models, it immediately meets the eye that the Biases were not zero, which is to be expected when using an independent test set. Furthermore, the root mean square error of prediction (RMSEP) values were between 0.77% and 1.90%. All but one RMSEPs were lower than their respective RMSECV. This was most likely due to the Kennard–Stone sample selection, whereby a set of samples was chosen to best represent the multivariate space of the data. Because of that, high value samples, as well as low value samples, were selected in order to best describe the multivariate space. Hence, the error of the calibration set was higher, since it included more extreme values. The independent test set, on the other hand, could then easily be fitted into the well-described multivariate space. Furthermore, all RMSECV values were close to their respective RMSEP values, indicating a robust model.In general, the best model for the estimation of fat content was the model developed for NIRFlex N-500 data when measuring grated pieces of cheese. The cross-validation and the independent test set-validation both had a coefficient of determination of over 0.99, and the RMSECV and RMSEP were both under 1%. Furthermore, the RMSECV and RMSEP values were close, indicating a robust model. Additionally, the statistical parameters suggested that the analysis of fat content for grated pieces of cheese was slightly better. This was most likely due to the fact that whole pieces of cheese can be spatially inhomogeneous.For measurements with the NIRFlex N-500, three principle components were needed to develop models for moisture content for whole pieces and grated cheese. For data recorded with SCiO, two principle components were needed to develop models for moisture content. The benchtop device recorded spectra in a broader range than the SCiO, which is why multiple O-H vibrations could be found. However, these vibrations did not only stem from water, but also from other components. The SCiO on the other hand, recorded spectra in a very narrow range, hence only one O-H vibration was visible. Since the spectra of the NIRFlex N-500 were more complex, more principle components had to be used. The number of PCs did not differ greatly though, hence the results could still be compared.The coefficients of determination for all models were between 0.9327 and 0.9873. This indicated a high correlation between the spectra and the moisture content. The RMSECV values of the cross-validated models were around 1%, with the highest being 1.22%, and the lowest being 0.69%. The RMSEP values for the test set-validated models were also around 1%, with the exception of RMSEP for data recorded with SCiO for grated cheese, which gave a value of 1.71%. Only one RMSEP value was lower than the respective RMSECV value. In general, an error of about 1% for moisture content was quite low, considering that the mean of the reference data was 38.42%.The difference in prediction accuracy for moisture content of grated cheese and whole pieces of cheese was smaller than for fat content. The prediction of moisture content seemed to work better on grated cheese when working with the NIRFlex N-500; however, when using SCiO, whole pieces showed better prediction results.SCiO only offers a limited amount of statistical properties. It does not list coefficients of determination for test set-validated models or the Bias of any model. The algorithm for the validation process is proprietary and is; therefore, not accessible for users. The calibration model was validated using leave-one-out cross-validation (LOOCV) and then a test set was also used to validate the model. The coefficients of determination for all cross-validated models were between 0.972 and 0.988, indicating a high correlation between spectra and reference values. Additionally, all root mean square errors (RMSEs) were around 1%, with the lowest being 0.834% and the highest being 0.950%. All but one standard errors of prediction (SEPs) were lower than their respective RMSEs. The lowest SEP was achieved for the prediction of fat content in grated cheese, at 0.779%. The highest SEP was 1.102%. These results were in accordance with the analysis of the same data set using the software The Unscrambler X version 10.5. However, interestingly, the prediction of the fat content with the benchtop device yielded a much higher SEP. Furthermore, different spectral pre-treatments were needed. This is most likely due to the limited options in SCiO Lab. Additionally, SCiO Lab suggested four principle components for all models. Conducting a manual analysis with the Unscrambler X; however, showed that only two PCs explain over 90% of the variance of the original data set. This suggests that the automated algorithm in SCiO Lab takes in too many PCs. This might also be the reason why some of the SEPs were lower for the SCiO Lab web application.Additionally, it was attempted to imitate the results from the SCiO Lab web application with the Unscrambler X. However, since it was unknown how many smoothing points the SCiO Lab web application applies, results could not be duplicated. The SEP for fat content for whole pieces of cheese was 0.844%, and for water content, 0.825%. This means that for moisture the imitated results, using Unscrambler X, yielded better results, but for fat content the SCiO Lab web application yielded better results. Looking at grated cheese, the SEP for fat content was 0.771% and for moisture content it was 1.400%. This means that the SEP for moisture content was higher than predicted with the SCiO Lab web application, but the fat content was almost the same.For the analysis of the performance of the pre-established model in the SCiO app, all values were registered in Microsoft Excel and then SEPs and Biases were calculated .The Biases for moisture content were quite high, indicating a systematic shift between the data in the pre-established model and the recorded data. The SEPs were all around 1%, but slightly higher than for the self-developed models. This is most likely due to the fact that the model of the SCiO app was developed for “dairy products”, including yoghurt, cheese and puddings.Reviewing all SEPs and RPDs in Four models reached an RPD value of over 10: The model developed using the SCiO data for data analysis with The Unscrambler X for fat content of grated cheese (10.398); and the models developed using SCiO data for data analysis with the SCiO Lab web application for fat content of whole pieces and grated cheese . The highest RPD was yielded for data of fat content collected with the NIRFlex N-500 of grated cheese (14.022). Furthermore, four models had a value between 3 and 5: The models developed using data recorded with SCiO for moisture content of whole pieces of cheese (4.341) and grated cheese (3.208). Additionally, the RPDs for the SCiO data, recorded and evaluated using the SCiO App, was around 4 for moisture content of whole pieces of cheese (4.021) and grated cheese (4.681). All remaining models had an RPD of 5 or higher, indicating that the performances were satisfactory or even good.Regarding the SEP values, the results for fat content were, overall, better than for moisture. Overall, the measurements of grated cheese seemed to work better, most likely due to the more homogenous nature of grated cheese. However, results did not improve too much, which leads to the question as whether or not the sample preparation is really necessary.Forty-six cheese samples were analysed for their fat and moisture content using NIRS, as well as traditional wet chemical methods. Twenty samples were classified as hard cheese according to their water content of the fat-free mass, the other 26 samples were classified as semi-hard cheese . Upon reAll samples were measured, as grated cheese and whole pieces, with the NIRFlex N-500  and SCiO . The latter is a pocket-sized molecular sensor and was launched in 2016. The former is a modular benchtop device.−1. The digital resolution was 4 cm−1. All samples were measured at four spots three times, in the range 800–2500 nm (10000–4000 cm−1), with 64 scans in diffuse reflection mode. Hence, a total of twelve spectra were received for each sample.For measurements of the whole pieces of cheese, the NIRFlex N-500 was operated with the Fibre Optics Solids module. The fibre had an outer diameter of 4 mm and a spectral resolution of 8 cm−1), with the SCiO Lab app. The medium resolution of SCiO was 13 cm−1, with the lowest resolution (18 cm−1) being found at high wavenumbers and the highest resolution (9 cm−1) at low wavenumbers.With SCiO, the pieces of cheese were measured at six spots one time in diffuse reflection mode, in the wavelength range 740–1070 nm (13514–9346 cm−1). The spectral resolution was again 8 cm−1 and the digital was 4 cm−1. The cuvette was constantly rotated during measurement and each sample was analysed six times.For measurements of grated cheese, a cylindrical quartz cuvette  was filled with cheese and used for measurements. Thus, a constant measuring angle and method was assured. Grated cheese was analysed with the NIRFlex N-500 in diffuse reflection mode, with 64 scans in the wavelength range 800–2500 nm (10000–4000 cm−1) and the medium resolution was again 13 cm−1. All measurements were taken in diffuse reflection mode.For analysis of grated cheese using SCiO, the quartz cuvette was put onto SCiO and each sample was measured six times using the SCiO Lab app. The cuvette was manually rotated between measurements. Spectra were recorded in the range 740–1070 nm . Spectra recorded with SCiO, using the SCiO Lab app, were also evaluated using The Unscrambler X version 10.5; however, in addition, they were also analysed using the cloud-based web-application SCiO Lab . It offers a limited amount of multivariate data analysis options and was developed for laypeople. SCiO Lab, itself, suggests a certain set of pre-treatments; however, the user can also, him- or her-self, decide which pre-treatments to use when turning on the expert mode. In SCiO Lab it is possible to conduct a standard normal variate (SNV) and to calculate first and second derivatives. Furthermore, the average spectrum can be subtracted and the logarithm can be calculated. It is also possible to select a wavelength range. Additionally, the results given by the SCiO app were transferred to Microsoft Excel  for statistical analysis.−1 (1656–1838 nm) and from 8548 to 8076 cm−1 (1170–1238 nm). For moisture content, no wavenumber range was selected, as this did not improve results.Spectra recorded with the benchtop device were evaluated using The Unscrambler X version 10.5. For whole pieces of cheese, the twelve spectra of one sample were first averaged by a factor of four, in order to obtain three representative spectra per sample. Next, descriptive statistics, which is an implemented tool in the software, was applied to identify necessary spectral pre-treatments. Standard normal variate (SNV) was applied to reduce multiplicative scatter effects. No other pre-treatments were necessary as they did not improve the model much. Regression models for fat and moisture content were then established. The regression model for fat content considered only the regions in the spectrum where C-H vibrations occur. The two regions considered reached from 6040 to 5440 cmEvaluating spectra of grated cheese recorded with the benchtop NIRFlex N-500, the same route of identifying necessary spectral pre-treatments was taken. First, the six spectra of one sample were averaged by a factor of two, in order to again receive three spectra per sample. Next, descriptive statistics was applied and SNV was again used to remove scatter effects. The same wavenumber region as before was used to establish a regression model for fat content. For moisture content, the whole spectral range was used.−1) was used.Spectra of whole pieces of cheese and grated cheese recorded with SCiO were first analysed with The Unscrambler X version 10.5. All spectra were first averaged by a factor of two, in order to obtain three spectra per sample. Next, descriptive statistics was applied. Like before, SNV was identified as necessary spectral pre-treatment to remove scatter noise. For fat and moisture content, the whole spectral range  with cross-validation was applied to the respective calibration sets to establish calibration models. The models were then validated using test set-validation with the pre-established test sets.In the SCiO Lab web application, the calibration set was also used to build a PLS-R. Therefore, the data set was manually split into a calibration and validation set. Samples selected corresponded to the sample set created using the Kennard–Stone algorithm in The Unscrambler X version 10.5. Afterwards the developed model was validated using the test set.2 is a measure of the linearity, RMSECV and RMSEP are indicators of the accuracy of the established model and the Bias can point to methodical errors. The RMSECV is similar to a standard deviation, showing how great the differences between expected and actual values are. The RMSEP denotes the difference between the actual reference value and the predicted value by the established calibration model. Additionally, the RPDs [For the evaluation of the established PLS-R models, different statistical quality parameters were consulted. Rthe RPDs  can be uThis study examines the performance of a new hand-held NIR spectrometer, called SCiO, in comparison to a benchtop device. Forty-six different cheese samples—Grated cheese and whole pieces—were investigated in terms of their moisture and fat content. Additionally, different data analysis routes were taken in order to investigate if a deeper knowledge of chemometrics is necessary for the operation of SCiO, or if the limited tools implemented in the SCiO app and the SCiO Lab web application are sufficient to yield acceptable results.In general, the analysis of cheese worked better when investigating grated cheese, instead of whole pieces. Furthermore, all calibration models showed high correlation between spectra and reference data. All RPDs indicated that the developed models for whole pieces of cheese were satisfactory to excellent.This study shows that although overall results do improve by applying more sophisticated multivariate data analysis, the difference is only marginal. This implies that, in the near future, companies could easily use small and cheap NIR devices, with pre-established apps, for quality analysis. This is especially important for small businesses, as often appear in the cheese industry, because they are often unable to afford large instruments or an expert in data analysis.


Phaseolus vulgaris L.) by HPLC fractionation, revealing their ability to inhibit highly purified human recombinant aldose reductase (hAKR1B1). Six of these compounds were associated by MS analysis with the following saponins already reported in different Phaseolus vulgaris varieties: soyasaponin Ba (V), soyasaponin Bb, soyasaponin Bd (sandosaponin A), soyasaponin αg, 3-O-[R-l-rhamnopyranosyl(1 → 2)-α-d-glucopyranosyl(1 → 2)-α-d-glucuronopyranosyl]olean-12-en-22-oxo-3α,-24-diol, and soyasaponin βg. The inhibitory activity of the collected fractions containing the above compounds was tested for hAKR1B1-dependent reduction of both l-idose and 4-hydroxynonenal, revealing that some are able to differentially inhibit the enzyme. The present work also highlights the difficulties in the search for aldose reductase differential inhibitors (ARDIs) in mixtures due to the masking effect on ARDIs exerted by the presence of conventional aldose reductase inhibitors. The possibility of differential inhibition generated by a different inhibitory model of action of molecules on different substrates undergoing transformation is also discussed.Seven triterpenoid saponins were identified in methanolic extracts of seeds of the Zolfino bean landrace ( In fact, they have been reported to act as anti-inflammatory, hypocholesterolemic, expectorant, vasoprotective, and immunomodulating agentsAs a result of their antioxidant and antidiabetic action, one of the emerging features of saponins is their ability to inhibit aldose reductase (AKR1B1). This enzyme (EC 1.1.1.21) catalyses the NADPH-dependent reduction of a wide range of hydrophilic as well as hydrophobic aldehydes. For several decades it has been investigated in order to find molecules able to inhibit its reducing activity on aldoses.Camellia oleifera as aldose reductase inhibitors (ARIs) on the rat lens enzyme has been reportedin vitro inhibition of aldose reductase in a crude liver homogenate and in vivo inhibition of the enzyme in streptozotocin-induced diabetic rats, were reported for a furostanol saponin and its derivatives, extracted from Balanites aegyptiaca,in vivo in diabetic rats, and in an in vitro model of diabetic peripheral neuropathyThe rationale behind these investigations is based on the apparent link, in hyperglycaemic conditions, between sorbitol over-production and reducing power failure due to the AKR1B1 action and the development in diabetic subjects of pathological states such as retinopathy, nephropathy, peripheral neuropathy, cardiac dysfunctions, and cataractsAlthough such reports in the literature highlight the ability of saponins to inhibit AKR1B1, to our knowledge no detailed kinetic study on these molecules has been carried out, and no evidence for their ability to act as aldose reductase differential inhibitors (ARDIs) has been reported.Differential inhibition concerns the ability of a molecule to exert its inhibitory action depending on the nature of the substrate the enzyme is working onPhaseolus vulgaris L.) revealing their ability to inhibit highly purified human recombinant hAKR1B1 and, for some of them, to differentially inhibit the enzyme depending on the substrate  undergoing reduction.In this work seven triterpenoid saponins were identified in methanolic extracts of seeds of the Zolfino bean landrace , d,l-glyceraldehyde (GAL), EDTA and DSC18 hydrophobic interaction cartridges Supelco Discovery were purchased from Sigma-Aldrich . NADPH, l-idose, soyasaponin Ba (SSBa) and soyasaponin Bb (SSBb) were supplied by Carbosynth ; YM10 ultrafiltration membranes were obtained from Merck-Millipore ; soyasaponin Bd (SSBd) was obtained from ALB Technology Limited ; PTFE filtration membranes 0.45 µm pore size were from Phenomenex Italy ; HPLC grade methanol, formic acid, and acetic acid were purchased from VWR . HPLC grade water (18 MΩ) was prepared by a Mill-Ω purification system . All other chemicals were of reagent grade.Bovine serum albumin (BSA), http://germoplasma.arsia.toscana.it/.Dry seeds of yellow Zolfino landrace were obtained from the farm Agostinelli Mario in Leccio-Reggello  and authenticity was confirmed by comparing their features with those registered in the “Regione Toscana” germplasm data bank (access VE_027): g, and the pellet extracted again at 4 °C overnight as above. All the solvents used in the subsequent manipulation of the extract (water or methanol solutions in water) contained 0.6% (v/v) final concentration of acetic acid. The supernatants of the two centrifugation steps were pooled, dried at room temperature by rotary evaporator, resuspended  in 10% methanol aqueous solution and filtered through 0.45 µm PTFE membrane filters.Dried bean seeds, mechanically disrupted by Ultra-Turrax, were subjected to extraction by the addition (5 ml/g) of 80% methanol aqueous solution containing 0.6% (v/v) of acetic acid. The suspension was stirred for approximately 5 h at 4 °C temperature in darkness, centrifuged for 10 min at 4 °C at 7000 On the basis of previous results of the fractionation of Zolfino extract on a reverse phase HPLC column through a methanol/aqueous acetic acid gradientThus, the extract was applied on 5 ml DSC18 hydrophobic interaction cartridges (1 ml sorbent bed) previously conditioned by sequential addition of 5 ml of the following solutions (each containing 0.6% acetic acid v/v): 100% methanol, water and 10% methanol. A volume of 2.5 ml of bean extract was applied to the cartridge, and a stepwise elution was performed by the sequential addition of 5 ml of the following solutions: water, 10%, 30%, 50%, and 100% methanol. The fraction eluting with 100% methanol was concentrated by a rotary evaporator, diluted with water (containing 0.6% acetic acid) to obtain 40% methanol in a final volume of 500 μl, and filtered through a 0.45 µm PTFE membrane. The sample obtained is referred to hereafter as the “enriched extract”.The “enriched extract” was fractionated by a SpectraSystem HPLC instrument , equipped with a Kinetex C18 column, 250 × 4.6 mm ID, 5 µm particle size . A mixture of methanol with 0.6% acetic acid (solvent A) and a 0.6% v/v aqueous solution of acetic acid (solvent B) were used as the eluent. The gradient profile was as follows: 0–6 min, 40% A, isocratic mode; 6–26 min, 40–100% A; 26–36 min, 100% A, isocratic mode. Elution was performed at a flow rate of 1 ml/min; the absorbance at 254 nm was monitored online. The column was loaded with samples of about 1 g equivalent of the initial dry seed weight per run. The eluate was collected by splitting it into 18 fractions, 2 ml each.hAKR1B1 inhibition ability.Individual fractions (F1–18) were dried and resuspended in 0.1 ml methanol and assayed (15 µl) for tR) from 24 to 26 min (F13) were dried, resuspended in 55% methanol with 0.6% acetic acid, and filtered through 0.45 µm PTFE membrane. They were further purified by HPLC in the same conditions described above, except for the elution programme, which was as follows: 0–4 min, 55% A, isocratic mode; 4–34 min, 55–100% A; 34–44 min, 100% A, isocratic mode. The solvent eluting from the column between 22 and 28 min was split into fractions, which were named F13a, F13b and so on, and assayed for hAKR1B1 inhibition ability. In order to optimise the purification of the putative compounds with differential inhibition activity, the number of fractions collected and the pattern of separation was fine-tuned on the basis of the results of the bioassay. This led to the isolation of five fractions (F13a–F13e), which were collected according to their chromatographic profile thereby obtaining sub-fractions F13a–e.Aliquots of the HPLC fraction eluted with a retention time (HPLC-photodiode array (PDA)/UV-electrospray ionisation (ESI)-tandem mass spectrometry (MS/MS) analyses were performed using a Surveyor LC pump, a Surveyor autosampler, coupled with a Surveyor PDA detector, and an LCQ Advantage ion trap mass spectrometer  equipped with Xcalibur 3.1 software. Analyses were performed on a 4.6 × 150 mm, 4 µm, Synergi Fusion-RP column , using a mixture of methanol (solvent A) and a 0.05% v/v aqueous solution of formic acid (solvent B) as the eluent.m/z 150–2000, using N2 as the sheath and auxiliary gas.The gradient profile was as follows: 0–6 min, 40% A, isocratic mode; 6–28 min, 40–100% A; 28–36 min 100% A, isocratic mode. Elution was performed at a flow rate of 0.8 ml/min with a splitting system of 2:8 to the MS detector (160 μl/min) and the PDA detector (640 μl/min), respectively. MS/MS analyses were performed with an ESI interface in positive ion mode with a scan range of The parameters used for MS operating conditions were optimised as follows: capillary temperature, 270 °C; sheath gas flow rate, 60.00 arbitrary units; auxiliary gas flow rate, 3.00 arbitrary units; capillary voltage, 32.00 V; tube lens offset, 10.00 V; spray voltage, 4.50 kV. PDA data were recorded with a 200–600 nm range. Analysed fractions 13a–e were first dried using a Speedvac concentrator, then dissolved in methanol at a final concentration of 2.0 mg/ml and centrifuged; a volume of 20 μl of supernatants was injected into the LC-MS system.340=6.22 mM−1 cm−1) through a Biochrom Libra S60 spectrophotometer. In a 0.25 M sodium phosphate buffer pH 6.8, the standard assay mixture contained 0.18 mM NADPH, 0.4 M ammonium sulphate, 0.5 mM EDTA and 4.7 mM GAL. One unit of enzyme activity is the amount that catalyses the conversion of 1 µmol of substrate/min in the above assay conditions. These assay conditions were also adopted to assess the effectiveness of inhibitors when l-idose or HNE were used, at the indicated concentrations, as substrates instead of GAL. Differential inhibition (DI) refers to the difference between the percentage inhibition observed using l-idose and HNE as substrates in the assay conditions indicated.The AKR1B1 activity was determined at 37 °C as previously describedhAKR1B1) was expressed and purified to electrophoretic homogeneity as previously describedThe human recombinant AKR1B1 . Results are reported as the mean of the values and the 95% confidence interval from at least three independent measurements.The protein concentration was determined by the Coomassie blue staining methodWe recently reported that components of a methanolic extract of Zolfino bean, eluting as the most hydrophobic fractions from an HPLC reverse phase column, act as a promising source of ARDIstR from 24 to 26 min (l-idose and HNE reduction versus the amount of F13 (expressed as absorbance units at 254 nm/ml assay). The differential inhibitory activity of the sample with respect to the reduction of the two substrates with an IC50 for HNE  was clear, and was approximately two fold higher (*p < 0.05) than that measured for l-idose .Components eluting with o 26 min , F13, whtR from 22 to 28 min, namely 13a–13e (Panel A), were manually collected, dried and analysed for differential inhibitory ability (Panel B). As reported in hAKR1B1. Fractions 13 b and 13d appeared to be the most powerful inhibitors, with a comparable effectiveness in inhibiting l-idose reduction. However, when considering their differential inhibitory ability (DI) between l-idose and HNE, despite comparable values , statistical significance  was observed only for data referring to 13 b. The remaining three fractions  showed a lower inhibitory capacity and no significant evidence of differential inhibition.A further HPLC fractionation of F13 (see Methods) separated the suitable sub-fractions for the evaluation of the inhibitory capacity and for component identification. Thus, fractions with  13a–13e , Panel A ability , Panel BIn order to reveal the chemical structures of their bioactive components, sub-fractions 13a–e were analysed by means of HPLC-PDA/UV-ESI-MS/MS techniques.1–7) in all the analysed fractions, whose peak distribution (identified with the same corresponding numbers) is illustrated in 3, were identified by comparing their spectral data (UV and ESI-MS/MS) with those reported in the literature1 and 2 were compared with authentic pure standards. Thus, the structures were assigned as follows: soyasaponin Ba (V), SSBa (1), soyasaponin Bb (I), SSBb (2), soyasaponin Bd (sandosaponin A), SSBd (4), soyasaponin αg, SS αg (5), 3-O-[R-l-rhamnopyranosyl(1 → 2)-α-d-glucopyranosyl(1 → 2)-α-d-glucuronopyranosyl]olean-12-en-22-oxo-3α,-24-diol (6), and soyasaponin βg, SS βg (7)  . All sap+ and sodiated [M + Na]+ ion molecular peaks. The analysis of the fragmentation patterns led to the identification of all the molecules as soyasaponins B and E, with two different aglycones (soyasapogenol B and E) and two different trisaccharide sugar chains attached to the C-3 hydroxyl position of the aglycone moieties. Furthermore, saponins 5 and 7 showed the sugar 2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP) attached to the C-22 hydroxyl group.The molecular weights of all the constituents were deduced from the presence in the full mass spectra, acquired in the positive ion mode, of both protonated [M + H]1 ([M + Na]+ at m/z 981), compound 4 ([M + Na]+ at m/z 979), and compound 5 ([M + Na]+at m/z 1107), showed very similar fragmentation pathways, with diagnostic peaks at m/z 819, 657, and 481 (compound 1), m/z 817, 655, and 479 (compound 4), and m/z 945, 783, and 607 (compound 5) due to the sequential losses of one glucose ([M − 162 + Na]+]), one galactose ([M − 162 − 162 + Na]+]), and one glucuronic acid ([M − 162 − 162 − 176 + Na]+]) residues, respectively, thus confirming the presence of a trisaccharide chain linked to the aglycones. According to common fragmentation patterns observed for saponinsm/z 541 comprised of glucose, galactose, and glucuronic acid ([162 + 162 + 194 + Na]+), detected for the three compounds. In addition, other fragments corresponding to the trisaccharide chain sodium adduct ions were detected, due to the elimination of one molecule of water [162 + 162 + 194 – 18 + Na]+, a carboxylic residue [162 + 162 + 194 – 18 – 44 + Na]+, and the glucuronic acid unit [162 + 180 + Na]+. Thus, compounds 1 and 4 were identified as SSBa and SSBd, which differed in the aglycone moieties represented by the soyasapogenol B and soyasapogenol E, respectively. Compared to 1, compound 5 showed in addition a residue of 126 amu corresponding to a DDMP unit, and thus was characterised as soyasaponin αg.The ESI-MS/MS of sodium adduct ions  of compo2, 6, and 7 showed sodium adduct molecular ion peaks [M + Na]+ at m/z 965, 963, and 1091, respectively.The full mass spectra of compounds +, glucose [M–146 − 162 + Na]+, and glucuronic acid [M–146 − 162 − 176 + Na]+ units can be observed in the fragmentation patterns of all three precursor ions, indicating the presence of the same trisaccharide chain in these three saponins. This evidence was confirmed by the detection of fragments at m/z 507 ([146 + 162 + 194 + Na]+), 489 ([146 + 162 + 194 – 18 + Na]+), 445 ([146 + 162 + 194 – 18 – 44 + Na]+), 427 ([146 + 162 + 194 – 18 – 18 − 44 + Na]+), and 331 ([146 + 180 + Na]+) related to the glycosidic portion.Ion peaks corresponding to the subsequent losses of rhamnose [M–146 + Na]2 and soyasapogenol E for compound 6, as deduced from the presence of product ion peaks at m/z 479 and 481, respectively. Thus, compounds 2 and 6 were identified as SSSBb and 3-O-[R-l-rhamnopyranosyl(1→2)-α-d-glucopyranosyl(1→2)-α-d-glucuronopyranosyl]olean-12-en-22-oxo-3α,-24-diol, respectively. In addition, compound 7 was characterised by the presence of a DDMP residue and the same aglycone of 2, leading its structure being identified as soyasaponin βg. As an example, the MS/MS spectra of compounds 1 and 6 are shown in 3, which was detected in F13b coeluting with compound 2, showed a molecular weight of 970 amu, as deduced from both the sodium adduct [M + Na]+ and protonated [M + H]+ molecular ion peaks at m/z 993 and 971, respectively.The aglycones were established as soyasapogenol B for compound m/z 993 precursor ion suggested the presence of two hexose residues (peaks at m/z at 831 [M − 162 + Na]+ and 669 [M − 162 − 162 + Na]+) in the saccharide chain. However, the detection of the peak at m/z 609 ([M − 162 − 162 − 60 + Na]+) suggested the presence of an acetyl group. On the other hand, the base peak was represented by a product ion at m/z 555, related to the oligosaccharide chain, thus differing from both glycosidic portions linked to the other identified substances. Although the MS/MS analyses clearly indicated that compound 3 was a triterpenoid saponin related to the other analysed molecules, the complete assignment of its structure was not possible on the basis of the MS data. Thus, compound 3 remained unidentified.The analysis of the fragmentation pathway of 3 in 1, whose presence was revealed in both F13a and F13b, and compound 2, which was detected from fraction F13a to F13e, did not seem to exert any differential inhibitory action. This was evident from looking at the inhibition data of F13a in which these two compounds predominate. Similarly, the fact that fractions 13c, 13d and 13e, did not exhibit a significant differential inhibition, led the initial conclusion that also compounds 5–7 appeared to be devoid of a differential inhibitory capacity. This left the unidentified compound 3 and possibly compound 4 (SSBd) as the only potential ARDIs conferring differential inhibition to F13b.As highlighted by the LC-MS analyses , the inv1 and 2 is indicative of the difficulty in searching for differential inhibitors in multi-component mixtures. In this case, the availability of authentic standards of compounds 1 and 2 identified in the Zolfino bean  facilitated a detailed kinetic investigation in order to gain insights into the inhibitory features of these two unresolved components present in F13a.Such a conclusion, however, is debateable. In fact, the simultaneous presence in a mixture of more than one component capable of inhibiting the enzyme, may mask the possible presence of an ARDI. In this regard, the couple of compounds KM values. This to allow the enzyme to act on each substrate in comparable conditions. In the present study the concentrations of the two used substrates were chosen being close to their KM values, i.e. 0.04 mM for HNEl-idosel-idose and HNE reaction with essentially the same effectiveness, with an IC50 of 40 µM  and 47 µM , respectively. Conversely, SSBb, although less efficient than SSBa as an inhibitor, showed a differential inhibitory action between l-idose and HNE, since the IC50 for HNE reduction  was two fold higher than that for l-idose reduction .To compare inhibition curves of different substrates, the substrates concentration must be kept at values equal or equally proportional to their respective iK’) and the binary enzyme-inhibitor complex dissociation constant (iK) values. SSBa, acting as a mixed inhibitor, is characterised by rather low values of iK and iK’ which are essentially identical for both substrates, thus justifying its higher efficiency as an ARI with respect to SSBb and, at the same time, its failure to exert any differential inhibitory action (Panel A). Conversely, SSBb acts on the two substrates by exhibiting a different inhibition model, behaving essentially as a purely non-competitive inhibitor with respect to l-idose, and displaying an uncompetitive mode of action with respect to HNE.y action , Panel A4) that we used to verify the possible contribution of this molecule to the differential inhibitory action of F13b, was revealed to be, in our hand, unsuitable. In fact, unlike other commercially available standard saponins used in the experimentation (i.e. SSBa and SSBb), the SSBd MS analysis failed to reveal either the expected compound or signs of possible saponin degradation products.These results, which exemplify the predictable masking effect of an ARI over an ARDI, clearly indicate that either the isolation of single components or the availability of authentic standards are necessary to ascertain the ARDI features of molecule mixtures. In addition, these results enable SSBb to be included as a potential contributor to the differential inhibitory action of fraction 13 b. Unfortunately, the commercial SSBd standard ) was exploited, which was derived in ES steady state conditions for a general mixed type of non-competitive inhibitionKM to 10 KM. The inhibition mechanism types, defined on the basis of the relative values of the dissociation constants, were: competitive (K’i/Ki = 102), uncompetitive (i/KiK’=10−2), and purely non-competitive (i/KiK’=1) (Panels A–C).This enabled the residual activity versus the inhibitor concentration curves to be generated at substrate concentrations ranging from 1/10 iK’ value for HNE reduction for SSBb is comparable to the constant referred to l-idose. However, the different mode of action towards the two different substrates shown by this inhibitor may generate a differential inhibitory action (Panel D). This result explains the observed differential inhibitory action in conditions, such as those adopted in In the present case , the K’iy action , Panel D3) present in the most differentially active sample derived from the Zolfino bean extract fractionation .Following previous reports for Zolfino bean extracts that show its potential to inhibit AKR1B1The comparison between SSBb, acting as ARDI, and SSBa, acting as ARI is interesting. In this case, while the aglycone scaffold, which is common to both saponins, may represent the basic structural element of the interaction between the inhibitor and the enzyme, the sugar moiety of these molecules may modulate the differential inhibitory action.Another aspect emerging from the present study is the possibility to extend the concept of differential inhibition to situations in which the inhibitor is active on different substrates, but through a different inhibitory model. In such a case, combined values of the concentration of the different substrates may generate differential inhibition.It is clear that it is difficult to identify ARDIs in mixtures in which conventional ARIs are also present. In fact, the differential activity of an ARDI can easily be masked by the inhibitory action of the co-occurring ARIs, as is the case with SSBb and SSBa , F13a. T


Increases in the terrestrial export of dissolved organic carbon (C) to rivers may be associated with additional loading of organic nitrogen (N) and phosphorus (P) to the coastal zone. However, little is known about how these resources interact in the regulation of heterotrophic bacterioplankton metabolism in boreal coastal ecosystems. Here, we measured changes in bacterioplankton production (BP) and respiration (BR) in response to full-factorial  enrichment experiments at two sites within the Öre estuary, northern Sweden. The BR was stimulated by single C additions and further enhanced by combined additions of C and other nutrients. Single addition of N or P had no effect on BR rates. In contrast, BP was primarily limited by P at the site close to the river mouth and did not respond to C or N additions. However, at the site further away from the near the river mouth, BP was slightly stimulated by single additions of C. Possibly, the natural inflow of riverine bioavailable dissolved organic carbon induced local P limitation of BP near the river mouth, which was then exhausted and resulted in C-limited BP further away from the river mouth. We observed positive interactions between all elements on all responses except for BP at the site close to the river mouth, where P showed an independent effect. In light of predicted increases in terrestrial P and C deliveries, we expect future increases in BP and increases of BR of terrestrially delivered C substrates at the Öre estuary and similar areas. Despite representing a small area of the global ocean, coastal zones are major hotspots for biogeochemical cycling of the macroelements such as organic carbon, nitrogen (N), and phosphorus (P) in marine environments. The availability of N and P is central to both algal primary production and bacterioplankton secondary production (BP), and thus, these nutrients support the basis of marine food webs, exerting bottom-up control of the structure and function of coastal ecosystems , 2. In aChanges in climate are predicted to result in a 30% increase in precipitation in the northern Baltic Sea region during the current century . SubsequBacterioplankton metabolism is often limited by C, N, and P in aquatic systems . To suppDespite expected increases in C, N, and P loading, few studies to date have tested how estuarine bacterial metabolism will respond, and how this response will vary temporally and spatially. Existing knowledge on C, N, and P controls on estuarine bacterioplankton metabolism is mostly based on correlational patterns, which do not express causal relationships nor identify the interactive effects of the different elements. Because coasts are highly heterogenic systems, there is need for a mechanistic understanding of resource control on bacterioplankton metabolism, allowing the extrapolation of coastal element cycling dynamics from small to regional scales. Enrichment bioassays can be applied to investigate patterns of resource limitation, yet few have been conducted in boreal estuaries. This limited knowledge precludes an adequate understanding of the C, N, and P cycling at the estuarine interface between land, ocean, and atmosphere compartments.In this study, we determined potential resource limitation patterns. We aimed to determine single and interactive short-term effects of increasing C, N, and P on estuarine BR and BP during one complete year. The study was performed in the Öre estuary , which is highly influenced by riverine DOM loadings, which are projected to increase further . Because2, mean depth of 16 m and a water volume of 1.0 × 109 m3 [The study took place at the Öre estuary, located in the northwestern part of the Bothnian Sea, which is one of the sub-basins of the Baltic Sea Fig. . Terrest× 109 m3 . Water r× 109 m3 . The sal2. The catchment is dominated by a large fraction of coniferous forests (approximately 83%), mainly Norwegian spruce (Picea abies L. Karst.) and Scots pine (Pinus sylvestris L.), and by a substantial fraction of mires . The catchment has low influence of industrial sources, agricultural areas, and wastewater treatment plants along the river [Freshwater inputs from the Öre catchment are unregulated, and average discharge is approximately 365 mm per year. The Öre catchment has a total area of 3002 kmhe river . Total rhe river .Two estuarine sites, which are part of the Swedish national water quality monitoring program, were sampled by the Umeå Marine Sciences Centre (UMF) at 1m depth seven times each during a year (from March 2014 until March 2015). Both sampling sites are located in the central part of the estuary, and the distance between the two sites is approximately 3 km. One site is closer to the Öre river mouth  were added to the 5-mL vials in order to obtain the desired concentrations. Amendments of C were made by adding C6H12O6 (glucose) to a concentration of 5 mg C L−1. Amendments of N were made by adding NH4NO3 (ammonium nitrate) to a concentration of 0.5 mg N L−1 and amendments of P were made by adding Na2HPO4 (disodium phosphate) to a concentration of 0.05 mg P L−1, while control treatments received no amendments. The three elements were added in concentrations sufficiently large to remain in excess throughout 72 h of incubation. We choose a 72-h incubation period since, based on previous studies, we observed that bacteria need approximately 48 h to reach peak BP, and in addition, bacteria need time to adapt to the experimental temperature. Salt was initially considered as a factor, as differences in salinity levels for samples taken during periods of high or low flow could impact microbial substrate uptake [−1, 0.065 mol KCl L−1, 0.015 mol Na2CO3 L−1, 0.265 mol CaCl2 L−1, 0.162 mol MgCl2 L−1, 0.183 mol MgSO4 L−1) and raised salinity by 5 units. We tested the effect of salt with a generalized linear mixed model  neither B7 , nor for BP for site B3  nor for B7 , we removed salt from the statistical analysis and pooled the replicates that did not contain additional salt with those to which salt was added. This resulted in a full-factorial design with three factors and quadruplicate samples .After arrival at the laboratory, the samples were filtered on a 1.2-μm “A/E glass” filter  using a peristaltic pump, which prevented microzooplankton and larger particles from passing through with bacteria . We thene uptake . To test2 sensing system  and placed in the dark at 20 °C. The system recorded oxygen concentrations every 5 min for 72 h. Estimates of BR rates were determined for each treatment and date as the average slope of four linear regressions. The respiratory quotient was set to 1, which is within the range of estimates for rivers [After preparation, the vials were tightly closed and connected to a dynamic luminescence quenching-based Or rivers , 37 and r rivers , 39.3H-leucine incorporation method Smith and Azam [3H-leucine was added to a final concentration of 30 nM . Leucine incorporation was determined by incubation for 1 h in the dark at 20 °C. Incubations were stopped with trichloroacetic acid additions of 5% (w/v). A bacterial pellet was formed by centrifugation for 10 min at 14,000 rpm and then rinsed with 5% TCA. After addition of 1.2 mL of scintillation cocktail (PerkinElmer), radioactivity was determined on a Wallac WinSpectral 1414 Scintillation counter (PerkinElmer). Incorporation of 3H-leucine into protein was calculated using an intracellular dilution factor of 2 [After terminating the BR measurements, 1.2 mL of each sample vial was pipetted into sterile 1.5-mL Eppendorf tubes which were then used to measure BP with the and Azam , modifieand Azam . The 3H-tor of 2 . One blaWe retrieved data on physicochemical and biological variables for the two sites from the Marine Environmental Monitoring Program and archived by SMHI. We also compiled data on river discharge from SMHI. Discharge was measured on a daily basis at the Torrböle station which is the closest monitoring station from the river outlet, located 25 km upstream .p value set to 0.05. Additionally, we explored the regulation of BR and BP by performing a principal component analysis (PCA) using XLSTAT Version 2017.5 . Missing data , over time. Time was included in the model as a random variable to account for temporal autocorrelation. The GLMM can be used to analyze nonnormally distributed data and has the potential to account for random effects terms, such as time . The sam3 s−1), and highest during winter (5.1) when low riverine inputs occurred (8.4 m3 s−1).Ambient conditions at the Öre estuary varied during the experimental period Table . ConcentF1,54 = 30.589, p < 0.001; Fig. F1,54 = 1.647, p = 0.205; F1,54 = 0.970, p = 0.329, respectively). At site B3, BR rates were also stimulated by C additions . In regard to BP, at site B7, P amendments alone had a strong stimulating effect, increasing BP by 1.8 times on average , while amendments of C had no significant effect . At site B3, single C additions stimulated BP .Responses of BR and BP to single and combined element additions varied qualitatively between the two study sites. Stimulation of BR in samples from site B7 occurred in response to single C additions  and additions of C+N+P increased BR by 5.7 times . At site B3, addition of C+N increased BR on average 2.6 times  and C+N+P additions increased BR by 5.5 times . Bacterial production at site B7 increased on average 1.8 times in response to N+P additions , and 2.2 times in response to C+N+P additions . At site B3, BP increased on average 1.4 times in response to C+N additions  and 3.6 times in response to C+N+P additions .Combined additions of C+N led to an average increase in BR rates of approximately 1.9 times at site B7 , thus related to high riverine discharge . Marginal mean increase for BR upon C addition was positively related to PC1; however, this relationship was only marginally significant . There was no correlation between BR marginal mean increase and PC2 .The PCA extracted two significant PCs which together explained 73% of the total variance Fig. a. The PCrge Fig. a. There The predominant qualitative patterns of the single and interactive effects of increasing C, N, and P on bacterioplankton metabolism in the Öre estuary has to our knowledge been determined for the first time in this study. While it is difficult to predict how climate change will affect the riverine export of bioavailable nutrients and organic carbon to estuaries, our results shed light on the role of resource availability on the regulation of bacterial metabolism, which can be used to speculate how estuarine systems will respond to different scenarios of DOM and nutrient increase. Our study shows that P is a key nutrient, controlling BP in the Öre estuary, as BP was found primarily limited by P. Bacterial respiration increased in response to additions of C alone, and particularly in response to the addition of C combined with P and/or N. Because increases in terrestrial C and P fluxes are expected in the area, an increase of BP is expected as well as an increase in BR of terrestrially derived C.Additions of C alone had a large positive effect on BR rates at both study sites, while additions of N and P alone had no effect. These findings agree with our first hypothesis by confirming the role of C as primary limiting element for BR , which iWe found that BP was stimulated by single P additions at site B7. This contrasts our second hypothesis that additions of P and C in combination are required to enhance BP at the Öre estuary, yet agrees with one previous report of P limitation of bacterial productivity in the Öre estuary . EmpiricCombined additions of C and nutrients  led to large increases in BR rates at both sites, where significant C × N × P interactions were also determined. These results confirm our third hypothesis of costimulation between the three elements. The fact that N and P (together with C) costimulated BR is not surprising, since N and P are critical nutrients to several structural and metabolic cell functions, such as energy transport and synthesis of proteins . In lineCombined element additions  led in general to increased rates of BP at both sites. While P was significant as main effect on BP at site B7 (no interaction), at site B3, we observed a positive three-way interaction between C, N, and P. The fact that P had a unique effect on site B7 indicates that riverine inflows led to a strong P-limitation at the site. Despite C sedimentation within the estuary being low , a largePatterns in resource limitation of bacterioplankton metabolism were further clarified in the PCA which made possible to visualize how our 14 samples related to the 13 variables representative of environmental conditions at the estuary Table b. The poThe main effect of C on BR  was negatively related to PC1, although this relationship being only marginally significant. During periods of low riverine discharge such as during winter Table b, C limiResource stoichiometry indicators such as total C/N/P ratios were high  in comparison to the C/N/P of natural aquatic and cultured bacteria 50:10:1; , 61, whiIn this study, we added specific compounds not to mimic the natural DOM, but rather to relieve resource limitation such that the maximum potential resource limitation could be determined. Thus, rates of BP and BR obtained are specific to the compounds utilized here and not representative of the whole range of compounds that can typically be found in the DOM. In addition, BR rates were obtained during a 72-h incubation performed at the 20 °C. It must be thus acknowledged that such rates cannot be directly transferred to natural boreal systems. The BP rates were however less influenced by the 20 °C incubation and compare to rates reported in other Baltic Sea nutrient enrichment experiments performed in situ , 64. TheAfter 72 h, it is possible that the composition of the bacterial community at the end of the experiment shifted from the initial community composition. In this study, we did not have the opportunity to determine potential changes in the bacterial community composition; however, we do not think that these changes impacted our results since our BP estimates are identical to BP estimates from in situ studies , 64. We http://www.smhi.se/klimatdata/klimatscenarier/Framtidens-klimat); thus, a uniform increase in terrestrial riverine runoff throughout the year should be expected [Global changes are anticipated for the next century, with varying nature and magnitude across different geographical regions . Togetheexpected . In thisexpected  study co2, an increase of CO2 emissions and of CO2 concentrations in the surface waters of the Baltic Sea should be expected. The increase of terrestrial DOM is expected to mainly favor the planktonic heterotrophs. Thus, an increased C metabolism through the microbial loop may possibly decrease the amount of energy transferred to higher levels of the food web [Increases in BR imply a larger DOM-induced dissolved oxygen removal oxygen removal from the Baltic Sea waters, which currently amounts to more than one million tons per year . Thus, tfood web .In summary, our study evidence the single role of C for BR limitation and the role of P for BP limitation. Thus, to the extent that terrestrial DOM brings an increase in bioavailable C, BR of terrestrially derived C will increase. In contrast, an increase of BP will require freshwater delivery of bioavailable P. Our results are in line with the increasingly recognized role of terrestrial DOM exports for the regulation of estuarine microbial heterotrophic metabolism, and critical for the prediction of the effects of climate change and potential feedback mechanisms at the coastal zone.


Nioghalvfjerdsfjorden is a major outlet glacier in Northeast-Greenland. Although earlier studies showed that the floating part near the grounding line thinned by 30% between 1999 and 2014, the temporal ice loss evolution, its relation to external forcing and the implications for the grounded ice sheet remain largely unclear. By combining observations of surface features, ice thickness and bedrock data, we find that the ice shelf mass balance has been out of equilibrium since 2001, with large variations of the thinning rates on annual/multiannual time scales. Changes in ice flux and surface ablation are too small to produce this variability. An increased ocean heat flux is the most plausible cause of the observed thinning. For sustained environmental conditions, the ice shelf will lose large parts of its area within a few decades and ice modeling shows a significant, but locally restricted thinning upstream of the grounding line in response. The Greenland Ice Sheet has increasingly lost mass over the past few decades, yet the contribution from glaciers in Northeast Greenland is difficult to quantify. Here, the authors show that the floating part of 79 North Glacier has continuously lost mass since at least 2001, with a very high annual variability. This region has the potential to rise global sea level by 1.1 m in the unlikely case of complete loss of this ice sheet sector1. The region also represents a major transport route for ice discharge into the Nordic Seas where it adds to the oceanic freshwater budget and large-scale circulation (for example see ref.2).Nioghalvfjerdsfjorden Glacier or 79 North Glacier has the largest ice shelf in Greenland with a length of more than 70 km and a width of about 20 km at mid-distance. Together with its neighbors Zachariæ Isstrøm and Storstrømmen, it is one of the major outlets of the North East Greenland Ice Stream (NEGIS), sharing a catchment area of almost 200,000 km² or 12% of the Greenland ice sheet area3, the mass balance of the upper parts of NEGIS was long considered to be close to equilibrium (for example see ref.4), while thinning was observed closer to the margin. It was assumed that the region was not much influenced by climate change until the beginning of the new millennium6. The area of 79 North Glacier has remained remarkably stable since the first observations of its calving front in 19067, with no signs of ice shelf break-up even during recent years. However, the upstream NEGIS sector has shown increasing thinning rates since 20067 and Zachariæ Isstrøm has experienced the disintegration of its frontal ice shelf and an increase in ice flux of 50% between 1976 and 2015. Currently, Zachariæ Isstrøm loses about 5 Gt year−11. Even though the impact of changes in environmental parameters is not known in detail, the increased thinning rates are likely related to increased air temperatures leading to higher melt rates and a reduction in summer sea ice concentration. This facilitates higher calving and retreat rates, associated with a positive feedback due to a retrograde bed slope and reduction in buttressing from the glacier margins. In addition, the entry of warm subsurface ocean water could intensify the mass loss by melting6.While other regions of the Greenland ice margin have already shown strong mass loss8) and the Arctic Ocean9, increasing Arctic surface air temperatures10 and more regular fast ice summer breakups since 200111 might affect this ice shelf in a similar way. The loss of the ice shelf might imply a reduction of the buttressing of the ice sheet, leading to enhanced ice discharge, progressive thinning and retreat of the grounding line in dependence of the bedrock geometry12. The ice sheet thinning at the grounding line leads to larger surface slopes and thus to enhanced ice flow, which will gradually spread further upstream until a new balance geometry is reached.This recent evolution of Zachariæ Isstrøm and its potential causes raises the imminent question about the future stability of its northern neighbor, the 79 North Glacier’s floating part. Recently observed warming of Atlantic Waters in the Nordic Seas  after the disappeared middle earth snake in Nordic mythology, due to its resemblance of a winding snake. The location of Midgardsormen and of the different data sets used in this study are shown in Fig. 14). Here, we present a time series of ice thickness changes dating back to 1998, with a temporal resolution of better than three years.To date, no detailed analysis of temporal ice thickness changes are available for 79 North Glacier, apart from short period remote sensing observations (for example see ref.15) re-grounding at a shallow angle with respect to ice flow on a lateral bedrock shoulder in the fjord , while the corresponding thickness change at the grounding line is 30%.−1 during 2006–2009. Between 2009 and 2012 the annual ice thinning intensifies again, reaching 9.0 m year−1 for the entire period (more than 12 m year−1 in 2009/2010), before it reduces again slightly to 6.5 m year−1 between 2012 and 2014. The analysis of the recent TanDEM-X elevation models provides additional information for the period 2014 until 2016, where the ice thickness is reduced by another 5.2 m year−1 in the region of Midgardsormen.Based on the analysis of the grounding line migration, the thickness change reveals the following pattern: In the late 1990s, the floating ice tongue seems to be close to an equilibrium with a minor change in ice thickness (in spite of the observed considerable grounding line migration between 1998 and 2001). In the first years of the new millennium, the situation changes considerably and an ice thickness loss of more than 12 m is derived for 2001/2002. After this period, the thinning gradually reduces to rather low values of 1.5 m yearThere are several potential reasons for this considerable volume loss of the ice shelf. A change in the horizontal velocity structure  could lead to dynamic thinning of the floating ice. Also, variations in the surface mass balance could induce higher thinning rates and thus a reduction of the ice thickness. Increasing melt rates, however, might also be induced by changes in the subglacial oceanic conditions (such as ocean warming).16, amounts to 859 ± 10 m year−1 for the period 1998–2001 and to 843 ± 15 m year−1 for 2009 until 2010. Also the along flow velocity gradients did not change significantly over this period, indicating that the calculated ice thickness change is not related to dynamic thinning of the glacier. The strain rate in the center of the ice shelf, south of Midgardsormen and between the two ice shelf cross profiles in Fig. −1 in 1998 to −0.0374 ± 0.0278 year−1 in 2009. This relates to an increase in dynamic thickening of 0.56 ± 0.43 m year−1 . Therefore, a change in ice dynamics cannot be the main reason for the observed ice thickness reduction of 91.1 m between 1998 and 2016.Ice velocities showed almost no change during the entire period since 1998. The mean ice velocity along the ice shelf cross profile at Midgardsormen, determined with the IMCORR correlation algorithm17. The recorded summer air temperatures indicate a tendency to higher surface melt rates in the recent years  is taken to be kice = 9.6 mm K-1 based on measurements by18 on Storstrømmen Glacier (Northeast Greenland). The degree day factor for melting snow (ksnow) is taken to be 40% of kice in accordance to19. We assume an average snow cover of 0.5 m thickness that needs to be melted before glacier ice is melting. Based on these assumptions, we find that the maximum year-to-year variation of surface ice melt is 1.4 m year−1 only , a thinning of the ice shelf would lead to a decrease in ocean temperature at the ice-ocean interface, if ocean properties remained unchanged. In contrast, the succession of CTD profiles shows an overall warming and thickening of the Atlantic Water layer in the cavity, such that temperatures at the respective depth of the MGO-grounding line increased by 0.2 °C between 1998 and 2014, despite its migration to shallower depth . We can confirm a similar magnitude of ice loss for the central part of the ice shelf and for a similar period. However, our study shows for the first time the temporal pattern in mass wastage for a period of 18 years. The uncertainties in surface elevation derived from remote sensing images and the lack of repeat elevation information make it impossible to infer the temporal evolution from existing remote sensing information before 2010. However, the combination of bedrock topography and lateral grounding line migration provides the necessary input for deriving such a time series back to 1998. We have shown that the ice shelf experienced a change from a state close to equilibrium to a state of successive thinning, where the interannual variability is large. For equilibrium conditions, the ice thickness remains constant because ice dynamic thickening balances the total melt rates of about 12 m year−1 in the region of Midgardsormen, while the thinning doubles during phases of maximum thickness loss in 2002 and 2010.Based on our observations and calculations, 79 North Glacier has lost almost one third of its thickness in the region of the 1998 Midgardsormen experiment between 1998 and 2016. Because the ice shelf is freely floating in the fjord, it can be assumed that this relative thinning is representative of a large part of the ice shelf. Otherwise, the lateral grounding line would not have moved up to shallower bedrock. The results are consistent with previous investigations concerning the bulk mass loss  changes little between the different years, but the increased meltwater input at the ice base drives a more vigorous sub-ice shelf circulation that accomplishes the additional heat flux of 0.7 ± 1.8 × 1011 W into the cavity. This results in a reduction of the cavity exchange time scale from about 120 ± 26 days to about 90 ± 35 days, which further increases the sensitivity of the ice shelf to ocean changes30. Thus, while our analysis suggests that the ocean is likely the main driver of the observed changes at 79 North Glacier, the regional dynamics that control the heat transport into the ice shelf cavity and other contributors, such as subglacial discharge induced by surface melt or geothermal heat flux will need further attention to fully understand the observed thickness evolution.However, the reason for the large interannual fluctuations of the thinning rates revealed by this study still need to be found. AW anomalies in Fram Strait take about 1.5 years and longer to reach the 79 North Glacier32 and poses the question if the disintegration of the ice shelf and its related consequences on the grounded ice are likely to happen in the near future. The ice thickness reduced by about 30% during the period 1998 until 2014. Compared with balanced conditions, implying a mass balance of −12 m year−1 in the region of Midgardsormen, the mean ice loss during the observation period results in an almost 1.5 times higher mass balance magnitude. The numerical simulations demonstrate that the thinning will lead to large areas of very thin ice, which are most likely unstable and large parts of the ice shelf will disappear during coming decades. Given that the environmental conditions already enabled ice thickness reductions up to 13 m within one year that process could be considerably faster for enhanced oceanic energy fluxes into the ice shelf cavity. Even though the consequences are serious for the neighboring part of the ice sheet, where the ice thins by about 200 m after the loss of the ice shelf, it seems that the increased fluxes will not reach far into the ice sheet during the next century, resulting in thickness losses in the order of 30 m about 20 km upstream. It needs to be considered, however, that our simple model setup is not appropriate to simulate the long-term feedback mechanisms. Therefore a more detailed investigation is required for investigating the long-term stability of the ice sheet in this sector of Greenland.Potential consequences for the future ice shelf stability: Despite the fact that 79 North Glacier has a more stable grounding line situation than Zachariæ Isstrøm (rising bedrock inland), the loss of the ice shelf might contribute to destabilize the entire, marine-based ice sheet sector. At the moment the ice shelf is well buttressed in the fjord and even the loss of the outer part would probably not change this. Without the buttressing effect of the floating ice tongue in the fjord, our simplified model approach demonstrates that the ice thickness strongly decreases and the grounding line retreats by about 10 km. This is comparable to the results of a recent study13. In addition to the regional mapping of the ice shelf geometry, a detailed seismic survey in 1998 across Midgardsormen , providing values for ice thickness and water depth below the ice shelf at the locations indicated in Fig. men Fig. , close tmen Fig. . In addiThese ground-based measurements are complemented by airborne ice thickness measurements that were carried out in 1997, using a low frequency ground penetrating radar system and covering a large part of the ice shelf and the adjacent grounded ice sheet . The lines of this data set, which are used in this study, are shown as dark blue lines in Fig. 16.The temporal evolution of MGO-ridge was tracked on scenes, selected from the Landsat archive, for the period 1998 until 2015 with roughly annual separation and an additional scene in 1994  of the radar system is about 0.84 m. The final accuracy, however, depends on the difference between the estimated and the wave travel speed in the ice column. Because there is no firn layer on the ice shelf, the radar wave velocity should be about v = 168 + -3.4 m μs−1 (2% error) and thus very close to the theoretical value33. A realistic error of determining the two way travel time to the ice/underground reflector is the half wavelength and thus about 1.7 m, or τ = 2.4 μs. Therefore the resulting error in the derived ice thickness is33).We estimated the reference surface elevation of the airborne measurements relying on the combination of aircraft GPS positions and travel times of the electromagnetic wave through air (using the first reflection from the ice surface). The accuracy of the ice thickness estimation depends on the center frequency of the radar system and the travel speed of the radar waves in the ice. With the assumption of a wave velocity of 168 m μsThe GPS positioning error during the airborne survey was about 40 m, which relates to a relative change in ice thickness of about 3.5 m for a mean bed slope of 5°. The resulting ice thickness error from the radar measurements is therefore 5.5 m.We infer ice thickness change from the floatation condition and thus the bedrock elevation at the identified grounding line position. In order to determine the accuracy of the bedrock elevation, also the surface elevation error of the radar data needs to be included. The surface elevation at the grounding line is derived from the ice thickness and the floatation criterion south of the MGO ridge. The best fit for the free-floating condition at the grounding line results in a residual mean square of the surface elevation of 1.45 m across the entire ice shelf profile. The precision of the surface elevation correction from the radar data to the digital terrain model on the ice-free grounded part of the profile, north of the MGO ridge, is in the order of 2 m. Therefore, the error of the surface elevation along the profile from the MGO ridge to the shoreline is within 2 m.The error of the bedrock elevation thus results to The reconstruction of the ice thickness based on bedrock elevation, MGO position and the floatation conditions thus is affected by a total error of 6.4 m.34) that were collected in the framework of Operation IceBridge in 2012 and 2014 across 79 North Glacier along an almost identical flight line. The accuracy of the ATM elevation model is given as 2 m. We reduce the absolute error of ATM by calibrating the data over stable ground with the surface reflection of the radar data from 1997.To assess temporal changes in ice thickness, the airborne measurements from 1997 are compared to more recent smoothed  surface elevation measurements from the NASA Airborne Topographic Mapper (ATM) (ILATM2_20120514_1357062 and were derived using the Integrated TanDEM-X Processor (ITP), the operational interferometric processor of the mission35. The absolute height error of the DEMs computed with ITP takes interferometric coherence and geometrical considerations into account37. As explained in detail in37 the TanDEM-X global DEM and all intermediate raw InSAR DEMs are affected by the absolute horizontal error, the absolute height error and the relative height error that describes local height variations. In the present study, which relies on TanDEM-X—TanDEM-X raw DEM differencing, we only quantify the absolute height error of each scene separately. The relative height error is estimated as random error together with the final elevation difference measurements. Moreover, the absolute horizontal error is negligible, because of the excellent geolocation accuracy of the ITP processor37.In addition, TanDEM-X bistatic data acquired on 8-01-2011, 14-11-2012, 8-12-2014, and 28-09-2016 were used for spatially distributed surface elevation information. This data set is characterized by effective baselines ranging from 182 to 75 m with a corresponding height of ambiguity of 38–113 m  of the plume perpendicular to the flow direction, which is important to provide area average melt rates for an uneven distribution of ice shelf area at different depths39. This is represented in the model by a non-dimensional parameter of change in ice shelf area as function of depth (dw/dz), which was computed by binning and normalizing the gridded ice draft data40 into 200 m depth bins. To account for the varying geometry of the ice base for different plume paths, a set of five representative profiles of the ice base slope were used , such that mean melt rates obtained from the 1998 CTD data match with the observed bulk thinning of the glacier.The oceanic forcing as a driver of basal melting is assessed through four conductivity temperature depth (CTD) profiles that have been measured inside the ice shelf cavity between 1998 and 2014 during similar seasons . This simple approach will provide the temporal variability of surface melt  mass balance as is required for present-day steady state conditions. This forcing corresponds to the observed mean thinning rates.To assess the impact of the observed ice shelf thinning, the open-source 3-D thermomechanically coupled ice-flow model Elmer/Ice45, the TanDEM-X global DEM37, the ice thickness distribution from from BedMachine Greenland version 3 . To avoid overfitting or over-regularization an L-curve analysis was performed to select the optimal parameters for the inversion. From the modeled velocities the 2D buttressing field is computed following43.For the buttressing field, we solve an optimization problem to infer basal friction and stiffening coefficients by matching modeled ice velocities with observed velocities (for example see ref.n = 3) and a linear friction law. The ice viscosity B and the linear friction coefficient β2 are estimated deploying standard inverse methods. The defined cost function considers differences with the observed surface velocity field. To avoid flow instabilities during transient runs, the oscillations occurring in the inverted 2D viscosity field are eliminated by defining areas of equal viscosity. The geometry evolution of the glacier is calculated deploying the thickness evolution equation:H is the glacier thickness, t the time variable, as and ab the surface and bottom mass balance.The calculations for the temporal evolution are based on the shallow shelf approximation , using a non-linear constitutive equation  is estimated using the thickness evolution equation. This is achieved by introducing the velocity field calculated with the flow model in Eq. , Weather observations from Greenland 1958–2014—Observation data with description. Landsat data, used for tracking Midgardsormen , are available from the USGS remote sensing data archive: https://glovis.usgs.gov/app?fullscreen=0. Surface elevation data for 2012 and 2014 are retrieved from the NASA Airborne Topographic Mapper (ATM) data repository: http://nsidc.org/data/ILATM2/versions/2#. The TanDEM global DEM, used for surface elevation information in the numerical modeling experiment, is available on request via a science proposal at the German Aerospace Center (DLR) only. Ice thickness information is taken from BedMachine Greenland version3: http://sites.uci.edu/morlighem/dataproducts/bedmachine-greenland/, while the surface velocity is used from http://cryoportal.enveo.at/data/ and https://nsidc.org/data/NSIDC-0670/versions/1.The seismic data across Midgardsormen, the CTD profiles and TanDEM-X surface elevation profiles used in this paper are available via the Pangaea data base . The airborne radar data are available from the Microwaves and Remote Sensing Division, Danish Technical University (DTU) on request. Meteorological data used for the surface mass balance calculations are available at the Danish Meteorological Institute: Supplementary InformationPeer Review File


We report a case of hardware failure after distal femoral osteotomy (DFO) with a broken screw pulled out from the locking hole and positioned within the knee joint.A 57-year-old man presented to our orthopedic outpatient department with 3-months history of an unusual painful swelling at the operated area following DFO. The leakage of joint fluid from the penetrated suprapatellar pouch was assumed to be the reason for this complication.The overall aim of this case report is to provide a lesson to budding surgeons who might experience a similar situation that cannot be easily explained, like the unexpected complication in the present case. The common complications of osteotomies around the knee are cortical hinge fracture, unexpected loss of correction or overcorrection, hardware failures, regional pain syndrome, delayed union or nonunion, neurovascular injury, and infection –4. We de2) initially presented to our outpatient clinic with complaints of lateral knee pain. Radiographs revealed lateral compartment osteoarthritis and valgus deformity of the knee joint. He underwent a closing wedge DFO  level. We encouraged him to perform partial weight bearing, and applied compressive dressing. At postoperative 3 months, he revisited our outpatient clinic on a wheelchair with similar symptoms. CRP level was normal and he was afebrile throughout the follow-up period. Radiograph revealed four broken distal fixation screws, with one screw pulled out and positioned within the knee joint. Computed Tomography (CT) revealed a widened gap and fractured lateral hinge (Fig. Re-operation was performed. The distal screws were removed first. Among the four screws, the head portions of the three screws were removed, however one screw could not be found at the previous surgical site. The tip portions of the four screws were removed using a coring reamer and the remaining hardware was removed. The osteotomy site had minimal stability. At the anterior portion of the osteotomy site, the patellofemoral joint had a connection with the operated site. The pulled-out portion of the distal screw was assumed to have moved into the joint via this path, further strengthening the reason why joint fluid was aspirated from an extra articular site. The connection was closed and arthroscopy was performed to remove the migrated screw Fig. . Two canIn this report, we described a case of hardware failure after closing wedge DFO. In the analysis of the complication associated with closing and opening-wedge DFOs, both these procedures have similar major potential complications such as fracture, hematoma, and pulmonary embolism. Minor complications such as stiffness and postoperative pain also appeared in both groups. The common complications were plate prominence, discomfort, or irritation over the plate . NonunioOutcome of DFO may be improved by the precise method of fixation. The DFO plate configuration is slightly anteriorly positioned, and distal fixation holes are limited when compared with conventional plates for distal femur fractures, which is curved posteriorly for fixation of the femoral condyle. This may affect fixation stability. Correct surgical indication and rehabilitation also influence the complication rate of DFO .With regards to the failure mechanism in our patient, the intraoperative stability and postoperative management were similar to other patients who underwent DFO. However, this patient experienced an unexpected migration of a broken screw to the knee joint. There was recurrent painful swelling of the knee, and aspiration from the operated site revealed joint fluid.In addition, there was atrophic nonunion with bony resorption occurring at the osteotomy site. These two occurrences do not bode well for hardware stability since hardware failure is related to mechanical instability, while bony resorption is a biological deficiency. Based on our assumption, the presence of joint fluid is an obstacle to bone union, leading to metal failure . TherefoThe present study was aimed at providing a lesson to surgeons who might experience a similar situation. We assumed that the causes of the complication were as follows: Firstly, there might have been penetration of the joint during the index surgery. This could have caused regurgitation of joint fluid to the osteotomy site, impeding bone union. Secondly, metal failure was a result of nonunion, with subsequent migration of broken screw into the joint.Postoperative knee swelling should be further investigated to ascertain the reason, as seen in this case whereby joint fluid aspirated from an extra articular operation site should raise the suspicion of an adverse cause. Finally, the most important message would be to preserve the suprapatellar pouch during DFO and to repair the capsule if breached.DFO is performed at the adjacent area of the knee joint, and surgeons should preserve the suprapatellar pouch and knee joint to prevent such unusual complication as occurred in our case.