Premutagenic damages may be repaired prior to cell division while the damages in the second and third groups are permanent and have the ability of transmission to daughter cells after cell division (Guy, 2005) (Fig. 1). Between chromosomal assessments, micronucleus has been recognized as the most reliable and successful test as verified by the Organisation for Economic Co-operation and Development (OECD). A micronucleus is referred to the third nucleus formed during the metaphase/anaphase transition of mitosis. The group of these cytoplasmic

bodies is called micronuclei having a portion of acentric chromosome or whole chromosome, which does not integrate in the opposite poles during the anaphase. This results in the formation of daughter cells without a part or all of a chromosome. Regarding sensitivity, reliability, and cost-effectiveness Ribociclib clinical trial of this test, it has been proposed as a biomarker for genotoxicity calculations, and has been used in different studies on pesticide-exposed populations. Most of these selleck kinase inhibitor surveys implied on the increased level

of micronucleus formation in people dealing with pesticides for a long time (Costa et al., 2011, Ergene et al., 2007 and Garaj-Vrhovac and Zeljezic, 2002). Sister chromatid exchange (SCE) or exchange of genetic material between sister chromatids is another testing for chemicals suspected to be mutagenic. Elevated level of SCE has been observed in some diseases, including Bloom syndrome and Behçet’s syndrome and maybe tumor formation. There are some reports on increased frequency of SCE in pesticide applicators who worked in agricultural fields (Carbonell et al., 1990, Rupa et al., 1991 and Zeljezic and Garaj-Vrhovac, 2002). Single-cell gel electrophoresis (SCGE) or Comet assay is a simple and sensitive testing for evaluation of DNA strand breaks

in eukaryotic cells (Dhawan et al., 2009). This technique has been frequently used for biomonitoring genotoxic effect of pesticides in a large number of studies most of which implicate on induction of DNA damage by VAV2 these chemicals (Grover et al., 2003, Mostafalou and Abdollahi, 2012c, Shadnia et al., 2005 and Zeljezic and Garaj-Vrhovac, 2001). Although, genotoxicity assays are among necessary tests applying for pesticides prior to introducing to the market, collected data from post-market monitoring studies have been evident for potential of allowed pesticides in induction of genetic damages. Considering genetic damages as one of the main events for cancer induction or development, further studies focusing on genotoxicity of pesticides, of course in appropriate models like exposure to their mixtures along with some other promoting factors, are required to understand the carcinogenic and tumorigenic mechanisms of pesticides (Table 3).

In order to assess the loss of CK from muscle cells, which indicates damage to the sarcolemma, in vitro assays were performed as previously described ( Melo and Suarez-Kurtz, 1988a and Melo and Suarez-Kurtz, 1988b; Melo et al., 1993 and Melo et al., 1994). Briefly, mouse EDL muscles were removed, weighed and bathed continuously with PSS. During the bathing, the muscles were exposed to B. jararacussu venom (25 μg/mL), E. prostrata extract (25–100 μg/mL) and/or dexamethasone (25 μg/mL) that were added to the PSS. Perfusion samples were collected at 30 min intervals during

2 h and replaced with fresh solution. The collected samples were stored at 4 °C and their CK activities were determined according to previously described procedures ( Melo and

Suarez-Kurtz, 1988a and Melo and Suarez-Kurtz, 1988b). Mice were killed selleck kinase inhibitor under anesthesia and each of their hemi-diaphragms together with their respective phrenic nerves were carefully removed and placed in an isolated organ-bath chamber containing PSS (Bulbring, 1946). This solution was continuously gassed with 5% CO2/95% O2 and kept at 36.0 ± 1 °C. The muscle tendon was attached to an isometric force transducer (GRASS – FT03) to register the twitch tension. The records were saved selleck screening library on the computer throw a data acquisition system (DATAQ – DI-148U) for posterior analysis. The resting tension was adjusted to 1.0 g. Indirect contractions were evoked by supramaximal stimulation (0.1 Hz; Epothilone B (EPO906, Patupilone) 3–5 ms; 30–60 V) applied to the nerve with an electrode and generated by an electric stimulator (GRASS – S48). The preparations were allowed to rest for 30 min before the additions of B. jararacussu venom (2.5–50 μg/mL) alone or together with E. prostrata extract (25–50 μg/mL) and/or dexamethasone (25 μg/mL) to the chamber’s solution. The twitch tension at time zero was taken as the reference, and the measurements of tension recorded at each 30 min intervals for 2 h were shown as % of the reference ( de Oliveira et al., 2003). Data were expressed as mean ± SEM,

and Student’s t-test was used for statistical analysis. The p value < 0.05 was used to indicate a significant difference between means. Perimuscular injection of B. jararaca and B. jararacussu induced muscle damage as measured by the increased plasma CK activity after 2 h ( Fig. 1). Mice injected with B. jararaca venom showed an increase in plasma CK activity from 138.8 ± 48.95 U/L in PSS group up to 829.58 ± 93.02 U/L, while in those animals injected with B. jararacussu venom plasma CK activity increased up to 1504.82 ± 336.90 U/L. Treatment with dexamethasone (1.0 mg/kg) did not alter the increase in plasma CK activity induced by these venoms. However, E. prostrata extract (50 mg/kg) pre-incubated with venom reduced 46.

0 ± 0.6 g), while sedentary Mas-KO mice did not significantly alter body weight (Table 1). In the sedentary Gamma-secretase inhibitor group, Ang

II levels in the blood of Mas-KO (141 ± 38 pg/ml; Fig. 1) were not significantly different from WT (105 ± 8 pg/ml; Fig. 1). However, Ang-(1–7) was significantly lower in Mas-KO (41 ± 6 pg/ml) as compared to WT (137 ± 9 pg/ml; Fig. 1). The ratio of circulating Ang II/Ang-(1–7) in the blood of Mas-KO mice was 3.5 while in WT it was 0.7, which pointed out for a strong unbalance in circulating RAS with a predominance of Ang II in Mas-KO. No differences were observed in the concentrations of angiotensin peptides in the LV [Ang II: 6 ± 2 pg/mg vs 5 ± 1 pg/mg in WT; Ang-(1–7): 33 ± 6 pg/mg vs 34 ± 4 pg/mg in WT; Fig. 1]. Analysis of mRNA expression in the LV showed a higher expression of ACE2 in Mas-KO mice (3.98 ± 0.68 AU vs 1.0 ± 0.16 AU in WT; Fig. 2), accompanied by no difference in the expression of ACE or AT1 receptor in comparison to WT (Fig. 2). In addition, while collagen I and fibronectin mRNA expression were not different, collagen III expression was significantly lower in Mas-KO (0.37 ± 0.02 AU vs 1.0 ± 0.1 AU in WT; Fig. 3). No differences were observed in body weight, cardiomyocyte diameter and LV weight in Mas-KO in comparison to WT sedentary animals (Table 1). Six weeks of physical training did not change the body weight of Mas KO or WT mice compared with pre-exercise values (Table 1). Physical training induced

similar increase (∼10%) in cardiomyocyte diameter in Mas-KO HIF inhibitor (11 ± 0.2 μm eltoprazine vs 10 ± 0.2 μm in sedentary Mas-KO; Fig. 4) and in WT (10 ± 0.2 μm vs 9 ± 0.2 in sedentary WT; Fig. 4). The change in cardiomyocyte diameter was accompanied by an increase in mRNA expression of collagen I, collagen III and fibronectin in Mas-KO mice. In WT mice there was a tendency to increase collagen I, however only fibronectin expression was significantly augmented (two way ANOVA; Fig. 3). Physical training induced significant increase in Ang-(1–7) in the blood of Mas-KO (491 ± 53 pg/ml vs 41 ± 6 pg/ml in sedentary Mas-KO; Fig. 1) and in WT mice (244 ± 33 pg/ml vs 137 ± 9 pg/ml in sedentary WT; Fig. 1). As seen in Fig. 1, this increase

was higher in trained Mas-KO (491 ± 53 pg/ml) in comparison to trained WT (244 ± 33 pg/ml). Interestingly, there was an increase in Ang-(1–7) levels (∼2 fold) in the LV only in trained WT (80 ± 16 pg/ml vs 34 ± 4 pg/ml in sedentary WT). In contrast, trained Mas-KO presented an increase in Ang II levels in the blood (331 ± 73 pg/ml vs 141 ± 38 pg/ml in sedentary Mas-KO; Fig. 1) and in the LV (62 ± 10 pg/mg protein vs 4.2 ± 0.61 pg/mg protein in sedentary Mas-KO; Fig.

2). Despite different approaches employed for detection and characterization of synovitis (e.g. imaging or histologic assessment), published studies provide evidence of a correlation between synovial inflammation and symptoms such as pain, in patients with knee OA. Torres L. et al. [107] investigated the relationship between knee pain and specific joint pathology detected by MRI in patients with knee OA. They noted that synovitis or effusion, as well as meniscal tears and bone marrow lesions, were among findings that best correlated with knee pain measured on a visual analog scale (VAS). Others [43] specifically

examined the relationship between pain and synovitis on MRI and noted that changes in pain scores over time varied with changes in synovitis, strengthening the notion of a causal LBH589 mw relationship. A similar association between pain and synovitis was reported more recently [4] using contrast enhanced MRI. In that study, higher grades of synovitis conferred a 9-fold greater risk (95% confidence interval 3.2–26.3) of having painful knee OA. Using serum HA as a marker of synovitis, Ishijima et al. [46] also demonstrated a relationship between synovitis and pain. We [87] contributed further evidence of an association between

synovitis (defined histologically) and knee symptoms measured by the Lysholm score (which measures pain, swelling, limp, locking, instability, and functional disability on a single scale) in patients with early knee OA undergoing arthroscopic click here meniscectomy. Synovitis has not only been related to knee pain, but also to knee joint function using objective outcome measures of walking and stair-climbing times [100]. One recent study of patients with end-stage knee OA undergoing joint replacement did not support a relationship between synovitis [64] and pain simply measured by a VAS. The reasons for this are unclear, but Thiamine-diphosphate kinase may be due to differences in patient populations

studied, or differences in symptom assessments. We speculate that at advanced stages of knee OA where denuded bony surfaces are abutting each other, pain and symptoms may have multiple origins related to extensive structural alterations. Despite some disagreement in the literature, the majority of available studies provide compelling evidence that synovial inflammation is a rationale target for therapeutic intervention to control joint symptoms in OA. Future work should help define specific patient populations for whom targeting synovitis may have the greatest benefit. In 2005, Ayral and colleagues published a study demonstrating a relationship between synovitis and progression of cartilage erosion [3]. This was a secondary analysis of 422 patients enrolled in a clinical trial with medial compartment knee OA who had been followed longitudinally for over one-year. Synovitis and cartilage integrity was documented by the visual appearance of the synovial membrane and cartilage surfaces during baseline arthroscopy.

In conclusion, we demonstrate that highly potent NS5A inhibitors disrupt MW formation independent of RNA replication and, therefore, at a very early stage of the viral replication cycle. Although the exact impact of these drugs on NS5A structure remains to be determined, the block of biogenesis of the membranous HCV replication factory likely defines a major mode-of-action of these clinically highly promising direct-acting antiviral drugs. The authors thank Stephanie

Kallis and Ulrike Herian for excellent technical assistance, Jacomine Krijnse-Locker for help with electron microscopy, Simon Reiss for the HA-PI4KIIIα construct, and Charles Rice for the 9E10 antibody and Huh7.5 cells. The PI4KIIIα inhibitor AL-9 was kindly provided by Francesco Peri (Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, Italy), Petra Neddermann, and Raffaele De NVP-BGJ398 cell line Francesco (INGM–Istituto

Nazionale Genetica Molecolare Romeo ed Enrica Invernizzi, Milano, Italy). The authors are grateful to the Electron Microscopy Core Facilities at EMBL Heidelberg and Bioquant and the Nikon Imaging Centre, Heidelberg, for providing access to their facilities and expert support. “
“Kirk Lin, Christopher F. Martin, Themistocles Dassopoulos, Silvia D. Degli Esposti, Douglas C. Wolf, Silvia D. Degli Esposti, Dawn B. Beaulieu, Uma Mahadevan. Pregnancy outcomes amongst mothers with inflammatory bowel disease exposed to systemic corticosteroids: results of the PIANO registry. Gastroenterology click here 2014 146;5(Suppl 1):S1 In the above abstract, Lilani Perera should be listed as the 6th author. The citation should correctly be listed as: Kirk Lin, Christopher F. Martin, Themistocles Dassopoulos, Silvia D. Degli Esposti, Douglas C. Wolf, Lilani Perera, Dawn B. Beaulieu, Uma Mahadevan. Pregnancy outcomes amongst mothers with inflammatory GABA Receptor bowel disease exposed to systemic

corticosteroids: results of the PIANO registry. Gastroenterology 2014 146;5(Suppl 1):S1 “
“Gao Q, Zhao YJ, Wang XY et al. Activating mutations in PTPN3 promote cholangiocarcinoma cell proliferation and migration and are associated with tumor recurrence in patients. Gastroenterology 2014;146:1397–1407. In the above article, in the legend for Figure 2, panels (C), (D) and (E) show a detailed view of the residues A90, A211, and L232, respectively. Panel (F) displays the full-length model of PTPN3 protein shows that residue L384 is located in a disorder region between the FERM and PDZ domain. In the main text, on page 1400, Figure 2B should be cited as Figure 2B–E and Figure 2C should be cited as Figure 2F. Also, in Supplementary Figure 1, the validation rate currently listed as “48.4%” should be “51.7%.

, 1998, and Vann et al. (2009). As a control, we also examined a region not previously implicated in processing specific item features, Pexidartinib research buy the motor cortex ( Auger et al.,

2012). In the first instance, we sought to ascertain if our ROIs were more engaged by permanent than non-permanent items, now that multiple rather than single items were being viewed. If so, this would accord with results from previous work (Auger et al., 2012). We used the MarsBaR toolbox (http://marsbar.sourceforge.net/) to extract the principal eigenvariate of the fMRI BOLD responses within the anatomically defined ROI masks for each subject. Responses within the RSC and PHC were significantly greater for stimuli containing 4 permanent items than for those containing none (collapsed across hemispheres, BOLD response in arbitrary units, mean difference in RSC .45, SD 1.05; t31 = 2.42, p < .02; mean

difference in PHC .55, SD .77; t31 = 4.02, p < .0001). However, using this mass-univariate approach, there were no significant correlations between responses in either of the regions and the number of permanent items in view (RSC: mean r = .13, SD .47; not significantly different selleck from 0: t31 = 1.577, p = .1; PHC mean r = .17, SD .51; not significantly different from 0: t31 = 1.937, p = .06). We then progressed with another method, MVPA, that has been found to be more sensitive in some circumstances to stimulus representations (Chadwick et al., 2012, Haynes and Rees, 2006 and Norman et al., 2006). We used this to assess whether patterns of activity in RSC and PHC contained sufficient information to decode the number of permanent items present for any given trial (for all 32 participants),

with five possible options: Carteolol HCl 0, 1, 2, 3 or 4 permanent (i.e., never moving) items in view. As in previous studies (Bonnici et al., 2012, Chadwick et al., 2011 and Chadwick et al., 2012), we first performed feature selection, the purpose of which is to reduce the set of features (in this case, voxels) in a dataset to those most likely to carry relevant information. This is effectively the same as removing voxels most likely to carry noise, and is a way of increasing the signal-to-noise ratio (Guyon & Elisseeff, 2003). Having identified participant-specific voxels within the ROIs which provided the greatest amount of permanence information, the final classification used only these most informative voxels. For the overall classification procedure, data from 2 sessions were used for feature selection, with the remaining independent third session’s data being used only for the final classification in order to avoid so-called “double dipping” (Kriegeskorte, Simmons, Bellgowan, & Baker, 2009).

Boys had greater variability than girls on the Verbal, Performance and Full Scale IQs and in six of the ten subtests. However, girls had greater variability than boys in Comprehension, Vocabulary and Block Design, and there was no difference in the variability of boys and girls on Similarities. Future studies might consider controlling for sociodemographic

variables to further validate this finding. Thanks are extended to the participating children and their families from Jintan City, and to the Jintan Cohort Study Group. Funding was provided by the National Institute of Environment Health Sciences (NIH/NIEHS, R01-ES018858; K02-ES019878-01), USA. None of the authors declare Akt inhibitor any conflicts of interests. “
“An error occurred in the Appendix of this article. The correct version is printed below. “
“Down syndrome (DS) describes a collection of disabilities that include mental retardation and motor incoordination. It is due to the inheritance of an additional copy of all or part of chromosome

21 (trisomy 21; OMIM ID: 190685) and occurs in different populations in 1 per 370 to 1700 live births (Cocchi et al., 2010, O’Nuallain et al., 2007 and Parker et al., http://www.selleckchem.com/products/Gefitinib.html 2010). Impaired motor coordination in DS is evident as limited fine motor control, delays in the acquisition of gross and fine motor skills, dysarthria (the unclear articulation of words), strabismus (squint), nystagmus (oscillating eye movements), and altered balance and gait (Frith and Frith, 1974, Henderson et al., 1981 and Spano et al., 1999; references in Galante et al., 2009). The lack of coordination and poor balance implicate dysfunction of the cerebellum, a key brain structure involved in the control of movement. This inference is supported by

the finding that in individuals with DS, the volume of the cerebellum and the density of GCs therein are reduced by one third and one quarter respectively (Aylward et al., Phospholipase D1 1997, Baxter et al., 2000, Jernigan and Bellugi, 1990, Pinter et al., 2001 and Raz et al., 1995). Moreover, modeling of the triplication of genes on human chromosome 21 in DS, by triplication of differing numbers of orthologous genes in mice, generates different mouse models (for example, Ts65Dn, Ts1Cje, Ts1Rhr, Tc1) with varying degrees of decreased cerebellar volume, lower GC density and altered behavior (Dierssen et al., 2009, Galante et al., 2009, Haydar and Reeves, 2011, Lana-Elola et al., 2011 and Moldrich et al., 2007). These changes may be accompanied by changes in cerebellar gene expression (Laffaire et al., 2009 and Moldrich et al., 2007) and in the number and morphology of Purkinje cells (PCs), the class of cerebellar neuron that integrates input from GCs, as well as other cells, and produces the sole output from the cerebellar cortex (Baxter et al., 2000 and Necchi et al., 2008).

27 The study subjects behave better under health education and close monitoring during the study period, like smoking cessation or protecting themselves in public activity, to avoid LTBI.10 Moreover, despite no significant difference in clinical characteristics between patients who completed

the three QFT-GITs and the drop-out cases, patients with negative QFT-GIT1 dropped out more than those with positive QFT-GIT. Therefore, the number of patients who may convert to positive in the following QFT-GIT test was reduced. The current strategy of defining the cut-off value for IGRA is based on the results of active TB patients Selleckchem BEZ235 and low-risk healthy subjects.28 Recently, a grey zone of QFT response has been PLX4032 ic50 proposed to

replace the cut-off value of 0.35 IU/ml in the general population.14 and 18 Although immuno-compromised hosts without any history of TB contact have a high risk of developing active TB and account for a major proportion of TB cases, there is no consensus on whether the result of IGRA should be interpreted as that in contacts. Consistent with a previous report in health care workers,15 the present study shows that the QFT-GIT1 response can discriminate between persistent QFT-GIT positive patients and reversion cases. Persistent positive QFT-GIT probably indicates patients with LTBI. Although there is no clinical outcome to correlate QFT-GIT response in this study, identifying persistent QFT-GIT positive patients is still of practical importance since it is associated with the subsequent development of active TB in the dialysis population, close TB contacts, and users of anti-tumor necrosis factor drugs.8, 13, 29 and 30 In patients receiving tumor necrosis factor-alpha (TNF-α)

inhibitor,17 a wider range (0.35–1.0 IU/ml) of QFT-GIT grey zone than that of the general population (0.35–0.80 IU/ml) has been proposed.14 and 18 In the present study, the first report involving a long-term dialysis Amino acid population, an optimal cut-off value for QFT-GIT to identify persistent QFT-GIT positive patients is 0.93 IU/ml, rather than the current threshold of 0.35 IU/ml. With 0.93 IU/ml as the new cut-off value, 67–79% of QFT-GIT positive dialysis patients can be excluded for follow-up. A higher cut-off value can possibly pick up a highly selected priority group for follow-up monitoring and preventive therapy for LTBI if resources are limited. However, future studies to investigate factors predicting a QFT-GIT result in the grey zone and long-term follow-up for the development of active TB is required for risk stratification because definite diagnosis of LTBI is currently lacking. The conversion rate of 7.7% within six months for dialysis patients is higher than that of health care workers (1.9–2.8%).15 and 31 As in previous studies, prior TB history may be a predictor of conversion.

20 × 0.20 m frame. Samples were taken and treated following standard guidelines for bottom macrofauna sampling (HELCOM 1988). The occurrence and importance of prey items were inferred from the analysis of fish digestive tracts. The former describes the relative frequency of a particular prey in all digestive tracts, while the latter indicates how much

a particular prey item contributes to the total content in a discrete digestive tract. Both parameters were divided into three categories: high, moderate and low. A ‘high’ occurrence means that a particular benthic animal is found in more than 50% of samples, ‘moderate’ – in 20–50% of samples and ‘low ’ in < 20% of samples. A ‘high’ importance means that most of the buy BYL719 digestive tract can be filled with a particular prey species (more than 50% of tract content), ‘moderate’ – 20–50% of tract content, while‘low ’ means that a particular item is only a small addition to the whole tract content (< 20% of tract content). The occurrence and importance of prey items are shown in Table 1. As the study aimed to evaluate the quality of the seabed for the feeding of fish, the assessment was based only on benthic invertebrates, excluding nectobenthic species and small pelagic fish. To predict the biomass Wnt inhibitor distribution of prey species the Random forests (RF) regression

model (Breiman 2001) implemented in the ‘randomForest 4.6-2’ package (Liaw & Wiener 2002) within the R environment was chosen. The modelling procedure was as follows. First of all, a correlation matrix was created for all predictors. If a correlation coefficient was > 0.7 or the VIF (variance inflation factors) were > 3, those predictors were not used for constructing

the model. Then the biomass data were split into two sets: train data (70% of all data) for constructing the model and test data (the remaining 30%) for validation. In order to avoid an uneven distribution of zero values the split was made semi-randomly: all sites were chosen randomly but with the proviso that sites with zero values would distribute 70/30 in train/test datasets. Parameters for Alanine-glyoxylate transaminase RF were selected as follows: the number of trees (ntree) was set to 1000, while the number of variables randomly selected at each node (mtry) and minimum node size (ndsize) were set to default values 2.3 and 5 respectively. After running the model the importance of the predictors was assessed. The Mean Decrease Accuracy (%IncMSE) was calculated to assess the importance of every environmental factor for the response variable. During validation, predicted values were compared with observations of external data (test dataset), thereby revealing the model’s true performance. Several estimates were calculated: (1) MAD – mean absolute deviation, (2) CVMAD – coefficient of variation of MAD, rs – Spearman’s correlation between observed (yt  ) and predicted ( y^t) values. equation(1) MAD=n−1∑t=1nyt−y^t, equation(2) CVMAD=ΜADy¯×100.

Seasonal changes are also clearly evident in the dependence of DOC concentration on time in the course of a year (Figure 5). In the non-growing

season, DOC concentrations do not exceed 3.5 mg dm− 3 while in the growing season they reach as much as 8.2 mg dm− 3. This supports the conclusion that here are two pools of dissolved organic substances, labile and resistant to biochemical oxidation. The labile fraction of DOC is supplied to seawater in the period of intensive primary production, whereas the stable form persists in seawater throughout the year. Fluctuations of DOC and POC in Baltic seawater were reported by Jurkovskis et al. (1976), Pempkowiak et al. (1984), Grzybowski & Pempkowiak (2003), Burska (2005) and Woźniak (2014), while Kuliński & Pempkowiak (2008) suggested the existence of two DOC fractions of varying biochemical stability. It has been speculated throughout this text that both DOC UMI-77 chemical structure and POC concentrations

are influenced by the activity of plankton. The idea is firmly established in the literature (Thomas and Schneider, 1999, Hagström Dabrafenib molecular weight et al., 2001, Stoń et al., 2002, Doney et al., 2003, Thomas et al., 2005, Sarmiento and Gruber, 2006 and Segar, 2012). Also zooplankton can influence organic carbon concentrations in seawater (Dzierzbicka-Głowacka et al. 2011). The abundance of plankton can be approximated by proxies: chlorophyll a, phaeopigment a ( Bianchi et al., 1996, Meyer-Harms and von Bodungen, 1997, Wasmund and Uhlig, 2003 and Collos et al., 2005), while the phytoplankton activity influences the pH of seawater ( Edman & Omstedt 2013). To find answers to questions regarding the factors influencing POC and DOC concentrations, chlorophyll a (Chl a) and phaeopigment

a (Feo) concentrations, pH and temperature of seawater were measured simultaneously with DOC and POC. The measured water properties were used as proxies of phytoplankton abundance (Chl a), photosynthetic see more activity of phytoplankton (pH), activity of zooplankton (Feo), and season (Temp) ( Voipio, 1981, Omstedt and Axell, 2003, Schneider et al., 2003 and Kuliński and Pempkowiak, 2008) The relationships between the concentrations of DOC and POC are presented in Figure 6. They are characterised by a coefficient of determination R2 = 0.61, which gives a coefficient of correlation R = 0.78. This strong correlation can be attributed to the composition of POM, comprising both phyto- and zooplankton – direct sources of DOC, and to the bacterial disintegration of detritus ( Hoikkala et al. 2012), also a component of POM ( Dzierzbicka-Głowacka et al. 2011). The relationships between DOC and POC and the other individual factors – chlorophyll a, phaeopigment a, pH and temperature (combined results for the Gdańsk, Gotland and Bornholm Deeps) – are presented in Figures 7 and 8 for DOC and POC respectively. The dependences were approximated by linear equations. The slope coefficients and coefficients of determination (R2) are listed in Table 5.