Two samples (B475 and B22) were highly active, as active as the s

Two samples (B475 and B22) were highly active, as active as the standard haemorrhagic

venom (B. jararaca). Venom samples from B208, B33, B67 and B5 were moderately active (compared to B. jararaca), while those from B8, B469 and A229 were of low haemorrhagic activity. Myotoxic activity was rare and usually mild. Only T224, T221 and T61 (fraction 20) were clearly myotoxic although B526 and T208 were mildly myotoxic. Oedema was common, but non-specific ( Table 1). Clear evidence of neurotoxicity was seen only with T61 (fraction 20) ( Table 1, Fig. S1). The total dataset contained 253 non-redundant protein sequences (Fig. 1). The alignment is available in the Dryad data depository (doi:10.5061/dryad.16pg7). The selleck inhibitor first four factors describing amino acid composition were retained. These Sorafenib cost principal components, referred to as PC1-4(comp) hereafter, summarised 16.7, 14.0, 10.1, and 9.5% of variation respectively. Ninety-five proteins had known functions that could be assigned to one of six major functions. However, anticoagulant and antiplatelet functions were subsequently combined into a “haemotoxic” category after preliminary analyses showed that no physico-chemical property or PC(comp) could distinguish between these groups (Tamhane’s post-hoc test). Final sample sizes were: Myotoxic: 30; Haemotoxic: 19; Neurotoxic: 26; Hypotensive: 7; Oedematous: 15. Neurotoxic PLA2s frequently also show myotoxicity

(Montecucco et al., 2008), but were classed as neurotoxic rather than myotoxic for the purpose of this analysis. Robust tests (Brown-Forsythe) for the equality of means showed all variables apart from PC2(comp) showed significant differences among groups. The four resulting discriminant functions (Table 2) contained 69.1, 13.5, 11.1, and 6.3% of variation respectively. Another 158 proteins which had no known function were plotted on the resulting axes (Fig. 2) and colour-coded by their posterior probabilities of belonging to one of the functional

groups (Table S2). All groups, except for haemotoxic and hypotensive proteins, were successfully discriminated on two axes (Fig. 2A). DF1 largely reflects the difference in pI, with haemotoxic/hypotensive proteins being acidic, myotoxic, neurotoxic and most oedematous proteins being basic. However, notably some oedematous Oxymatrine proteins can be distinguished from myotoxic ones by being more neutrally charged at pH7. DF2 (Table 2, Fig. 2A) largely distinguishes a smaller group of oedematous proteins on the basis of PC3(comp), with oedematous toxins having lower amounts of phenylalanine, arginine and tyrosine, and higher amounts of methionine and valine. DF3 (not shown) is influenced by a contrast between net charge and pI, and further distinguishes myotoxins proteins from oedematous proteins and neurotoxins, with myotoxins displaying a lower net charge for a given pI than the other types.


second carriage phenotype is long-term S aureus carr


second carriage phenotype is long-term S. aureus carriage, exemplified by the much slower loss of recruitment spa-types in recruitment-positives, and low loss rates >4–6 months after acquisition in both recruitment-positives and negatives. Our data could not fully support or refute the presence of a third “truly persistent” carriage phenotype as the proportion classified as consistent long-term carriers continued to decline as length of follow-up increased throughout the study. Further follow-up will be necessary to assess this definitively. Using our method of analysis, truly persistent carriage would be indicated by loss rates reducing to zero OSI-744 in vivo some time after 24–30 months (Supplementary Fig. 1(b)) with no further change in the proportion still carrying S. aureus (Figs. 4(b) and 5(a)). Other studies have defined “persistence” using more frequent sampling over shorter timescales, 11 and 12 sometimes using quantitative culture 27; when this study was set-up resource-constraints required a compromise between less intensive long-term versus more intensive short-term follow-up. One important study limitation is clearly the lack of a sampling point earlier than one month, e.g. at one week, which would Ribociclib research buy have enabled us to investigate “persistent” carriers defined using van Nouwen’s

rule. 12 The fact that these “persistent” carriers have been shown to differ significantly in characteristics such as clearance of a S. aureus inoculum, 19 and host genetics, 13 indicates Cediranib (AZD2171) that at least a subgroup form a distinct sub-population. However, we did have a sampling point at one month, and Fig. 5(a) demonstrates a clear ongoing linear decline in consistent long-term carriage even in those with two initial positive cultures, suggesting that a proportion with

“persistent” carriage will not carry S. aureus long-term. In fact five of 17 “persistent” carriers (29%, 95% CI 10–56%) were not carrying S. aureus eight years later in the original study of Van den Bergh. 11 Since van Nouwen et al. found that two qualitative and two qualitative + quantitative cultures had almost identical performance for classifying “persistence” in a validation set, 12 it is unclear that doing quantitative cultures in our study would have materially altered this finding; we prioritised spa-typing all isolates over such quantitative culture. Our findings suggest that “persistence” as previously defined12 and 27 could overestimate long-term carriage at the species level, and thus that there is no quick and reliable method to identify consistent long-term S. aureus carriers. Furthermore, 15% of long-term carriers at the species level in our study did not carry the same spa-type consistently (similarly to Ref. 28). Whilst colonised with S.

Overall, more unique orthologs (~ 6 ×) were expressed in the mous

Overall, more unique orthologs (~ 6 ×) were expressed in the mouse duodenal epithelia (1649 orthologs) compared to the rat (259 orthologs). Cross-species comparison of the jejunal gene expression identified similar numbers of unique differentially expressed orthologs (3782 rat and 3334 mouse) selleck chemical (Fig. 5D). As observed in the duodenum, overlapping orthologs increased from 1576 to 3864 genes with reduced stringency (Figs. 5E–F). Unlike the duodenum, the number of species-specific differentially expressed genes was comparable (971 rat vs. 705 mouse). Hierarchical clustering

of the duodenal (Fig. 6A) and jejunal (Fig. 6B) overlapping orthologs revealed that at ≤ 14 mg/L SDD clustering was more random. In contrast, differential gene expression at 60–520 mg/L SDD clustered according to species. Overall, the dose-dependent induction/repression was consistent between mice

and rats. However, divergently regulated clusters were also identified (Supplementary Fig. S3A). Cross-species analysis of day 91 duodenal responses identified 1504 and 3484 differentially expressed unique orthologs for the rat and mouse, respectively (Fig. 7A). Comparative analysis identified that 811 (|fold change| > 1.5, P1(t) > 0.999) and 2536 (|fold change| > 1.2, P1(t) > 0.9) orthologs overlapped between the species. The mouse duodenal epithelia expressed more Selleck Quizartinib (~ 5 ×) non-overlapping unique orthologs compared to rat following 90 days of SDD exposure ( Figs. 7B–C). Similar comparisons PRKACG of jejunal differential gene expression identified 1305 rat and 3620 mouse orthologs of which 729 were expressed in both species ( Fig. 7D). Using relaxed criteria, the overlap was comparable to the duodenal orthologs at day 91 (2772 jejunal orthologs in Figs. 7E–F). Hierarchical clustering of overlapping duodenal and jejunal orthologs again showed species-specific clustering at higher doses ( Figs. 8A–B, Supplementary 

Fig. S3B). Correlation analysis indicates that 81% of the overlapping differentially expressed duodenal orthologs exhibit similar patterns at day 91 (Fig. 9A, Supplementary Fig. S4). However, several divergently (induced in one species, repressed in other) expressed orthologs associated with the immune response (Ccl24, C3), ion transport (Slc25a25), and growth factor/cytokine signaling (Areg) ( Table 2) were identified and verified by QRT-PCR ( Fig. 9B, Supplementary  Fig. S5). Approximately, 61% (2536 out of 4177) of all orthologs were differentially expressed in the rat and mouse duodena. However, 1392 orthologs exhibited mouse-specific differential expression compared to only 249 in the rat (Fig. 7C). This qualitative (i.e. different genes/orthologs) and quantitative (number of species-specific differentially expressed orthologs) difference may contribute to the species-specific tumor outcomes. Hierarchical clustering of species-specific expressions revealed most genes exhibited weaker potency and efficacy in the rat compared to the mouse (Supplementary Fig. Fig. S6).


options Download full-size image Download


options Download full-size image Download as PowerPoint slide We were sitting my lab in early August last summer reminiscing. “Stanley”, I said, “How long did it take you to buy a pair of western boots?” (Referring to his first job at Rio Vista International after leaving ORNL – and me – in 1981. Rio Vista was a cattle ranch near San Antonio, Texas eager to conduct cutting edge research on cow embryo cryopreservation and transfer. They hired Stanley and Bill Rall for this purpose.) Stanley and I had just attended the 50th Anniversary meeting of the Society for Cryobiology in Bethesda in late July 2013, and he was visiting my lab to discuss our collaborative research under an NIH grant. He thought for a few seconds and said a bit sheepishly “Oh, about two days.” I replied “Did you ever see the movie with Venetoclax concentration Danny Kaye titled the ‘The Ceritinib chemical structure Secret Life of Walter Mitty’?” [This was based on a New Yorker short story of the same name by James Thurber]. “Well”, I continued, “You have certain Walter Mitty characteristics!” For a few seconds, Stanley looked like a deer caught in automobile head lights, and then he broke out into a broad grin. “You know,

Peter, I did see it, and you’re right!” I tell this little story not just because it’s amusing but because it says something important about how the man lived life and did science. Of course, it underlay his being the pre-eminent raconteur of cryobiology. At the memorial service organized by his daughter Beth and son

Jonathan in Houston April 5, all four speakers noted with great affection that conversations with Stanley would often be punctuated with “That reminds me of …” where the ellipsis represents any of perhaps two dozen tales in Endonuclease his repertoire. Most great American story tellers are native to the American South, but Stanley was more representative of the Herman Melville branch from the North–East (Rhode Island in Stanley’s case). I think his story-telling abilities lay at the heart of his enthusiasm for science in general and for the science and applications of cryobiology in particular. Unquestionably, this enthusiasm partly explains why he probably knew and worked with more cryobiologists world-wide than any one. It partly explains why he was so often invited to lecture globally and to organize and conduct workshops on the techniques for the freezing and vitrification of mammalian embryos and sperm. But there were aspects of his career that had little or nothing to do with Walter Mitty-ish characteristics. Far and away number one in my view is that he was a first-rate scientist! And almost equal to that is that the science he did has had a huge impact on the science and applications of cryobiology, and on the impact of that science on society. First, he had extremely high standards for the experiments he designed, conducted, and published.

In addition to preventing vertebral fractures, eldecalcitol reduc

In addition to preventing vertebral fractures, eldecalcitol reduced the incidence of wrist fracture, but had no significant effect on other non-vertebral fractures. There are two possibilities to explain at least a part of the effect on wrist fracture. First, we recently PD-0332991 manufacturer reported using clinical CT that eldecalcitol improved hip geometry better than alfacalcidol by increasing cross-sectional area, volumetric BMD, and cortical thickness by mitigating endocortical bone resorption

[20]. Therefore, eldecalcitol may have a better effect in improving biomechanical properties of long bones. However, direct assessment of the effect of eldecalcitol on radial geometry is required to clarify this issue. Second, although the incidence of falls was not monitored in the present study, there have been reports demonstrating

the effect of vitamin D supplementation check details or active vitamin D treatment in reducing the risk of falls [21] and [22], and the effect was mediated by an improvement of postural and dynamic balance [23]. In addition, higher serum 1,25(OH)2D3 concentrations were associated with lower fall rates [24]. Because vitamin D receptor-deficient mice exhibit vestibular dysfunction with poor balance/posture control [25], and because Bsm1 polymorphism of vitamin D receptor gene is associated with the risk of falls [26], the effect of vitamin D on vestibular function and falls appears to be mediated via vitamin D receptor. Thus, there is a possibility that eldecalcitol may have a stronger effect

than alfacalcidol in preventing falls. Further 3-mercaptopyruvate sulfurtransferase studies to compare the effect of eldecalcitol with that of alfacalcidol on the risk of falls can clarify these issues, as well as the reasons why eldecalcitol treatment reduced the incidence of wrist fractures. Serum 1,25(OH)2D was suppressed by about 50% in eldecalcitol group, probably due to the suppressive effect of eldecalcitol on 25(OH)D-1α-hydroxylase, while the suppression of serum intact PTH by eldecalcitol was less than that by alfacalcidol as reported previously [6] and [12]. Therefore, the stronger suppression of bone turnover by eldecalcitol cannot be explained by a suppression of PTH levels. Previous studies in animals revealed that eldecalcitol showed a stronger effect than alfacalcidol on bone compared with that on serum or urinary Ca [3] and [5]. Taken together, it is plausible to assume that eldecalcitol exerts a stronger suppression of bone turnover and a larger increase in BMD than alfacalcidol with similar effect on serum and urinary Ca, resulting in the superior effect in preventing vertebral and possibly wrist fractures. It should be noted that the suppression of serum intact PTH and BSAP levels was maximum after 6 months of treatment by both eldecalcitol and alfacalcidol, and both of these levels tended to rise after 6 months.

The apoptotic index of tumor-associated endothelial cells was det

The apoptotic index of tumor-associated endothelial cells was determined by co-localization of CD31 and TUNEL staining. Endothelial cells and DNA fragmentation in apoptotic cells were identified by red and green fluorescence, respectively, and apoptotic endothelial cells were identified by yellow fluorescence within the nucleus. Apoptotic tumor cells and tumor-associated endothelial cells were identified and counted in five random fields at × 400. Images were captured by an Olympus BX-51 microscope (Olympus America, Inc, Center Valley, PA). Tumor incidence, tumor weight, ascites volume (Mann-Whitney

U test), the number of PCNA-positive cells, and microvessel density (MVD; CD31/PECAM-1)

(unpaired Student’s t test) were compared in each treatment group. All values are expressed as means ± Proteasome inhibitor SD except where indicated. We determined the biologic effects of rhLK8 and paclitaxel on the growth of SKOV3ip1 human ovarian cancer cells producing high levels of VEGF injected into the peritoneal cavity of female nude mice. Paclitaxel significantly reduced tumor weight [0.04 g (0-0.2 g) vs 0.98 g (0.66-1.63g); median Epigenetics Compound Library research buy (range), P < .01)] and ascites [0.1 ml (0-0.2 ml) vs 0.9 ml (0.5-1.6 ml); median (range), P < .05] compared to control mice. No significant differences in tumor incidence or ascites volume were detected between control mice and mice treated with rhLK8 alone; however, rhLK8 significantly decreased tumor weight compared to the control [ Table 1; 0.65 g (0.01-1.3 Sirolimus in vitro g) vs 0.98 g (0.66-1.63 g); median (range), P < .05]. Combination treatment with paclitaxel and rhLK8 had an additive effect on reducing tumor weight [control group 0.98 g (0.66-1.63 g) vs combination group 0.01 g (0-0.14 g); median (range), P < .01)] and the volume of ascites

[control group 0.9 ml (0.5-1.6 ml) vs 0 ml (0-0.2 ml); median (range), P < .05]. The biologic effects of rhLK8 were also examined in mice injected with HeyA8 human ovarian cancer cells producing low levels of VEGF (Table 1). In these mice, tumor weight was significantly reduced by treatment with paclitaxel or rhLK8 alone compared to that in control mice [2.1 g (0-3.6 g) vs 4.0 g (0.2-7.2 g), P < .05 and 1.0 g (0-6.0 g) vs 4.0 g (0.2-7.2 g), P < .05, respectively]. Combination treatment with paclitaxel and rhLK8 had a significant and synergistic effect on decreasing tumor incidence (55.6 % vs 100%, P < .05) and tumor weight [0.3 g (0-2.4 g) vs 4.0 g (0.2-7.2 g), P < .01]. No substantial differences in the body weight of mice were observed among treatment groups (data not shown). VEGF levels were ~ 10-fold higher in SKOV3ip1 cells than in HeyA8 cells. Treatment of cells with rhLK8 for 48 hours had no significant effect on VEGF levels in SKOV3ip1 or in HeyA8 cells (Figure W1).

, 2004) The egg count

data of the no-choice bioassay wer

, 2004). The egg count

data of the no-choice bioassay were assessed using a generalized linear mixed model (GLMM) with binomial error distribution and log-link function to compare the ovipositing and non-ovipositing females (1/0), and a GLMM with Poisson error distribution and log-link function was used to evaluate the egg count data with the treatment as fixed factor. In a second Poisson GLMM model, the egg counts in the no-choice see more bioassays were evaluated with the incidence of mycosed females (infected/non-infected) and their longevity (up to 14 day s) as fixed factors. The number of eggs laid in the dual-choice bioassays of host and host patch quality were analyzed using a binomial GLMM for proportions. The Linear Mixed Effects “lme4” package was used to perform all GLMM including block as random effect. Data overdispersion was checked in all the models, but all values were below 2. Both fungal isolates were pathogenic to D. radicum larvae and T. rapae adults and increasing fungal concentrations resulted in an increase in mortality ( Table 1). For D. radicum larvae exposed to M. brunneum or B. bassiana, Galunisertib research buy the LC50 values were 2.44 × 106 and 1.08 × 107 conidia ml−1 while the LC90 values were 7.54 × 107 and 4.84 × 108 conidia ml−1, respectively. Inoculation of adult T. rapae with M. brunneum or B. bassiana resulted in LC50 values of 1.57 × 107

and 1.83 × 107 conidia ml−1 and LC90 values of 1.78 × 108 and 2.42 × 108 conidia ml−1, respectively ( Table 1). In the Cox model for survival of D. radicum larvae treated with different fungal concentrations no statistically significant differences were observed between the blocks, neither for M. brunneum nor B. bassiana ( Table 2). The concentrations of both fungal species had effects

on larval survival. With the concentration 1 × 106 conidia ml−1 as the Cox model baseline, there were significant differences compared to the concentrations 1 × 108 and 1 × 109 conidia ml−1 for both fungi ( Table 2). The hazard ratios (HR) increased with increasing fungal concentration while the MST of D. radicum decreased with increasing fungal concentrations SPTBN5 ( Table 2). At the highest concentration (1 × 109 conidia ml−1) the MST was 4 days for M. brunneum compared to 5 days for B. bassiana. Survival of T. rapae adults treated with different concentration of M. brunneum was not affected by experimental blocks or sex of parasitoids while there was a significant effect of fungal concentration ( Table 3). All concentrations were significant different from 1 × 105 conidia ml−1 as the Cox model baseline ( Table 3). For B. bassiana, no differences were observed between the blocks, but there was a significant difference between males and females ( Table 3). The life span over all fungal concentrations was (mean ± SD) for females 8.8 ± 2.5 days and for males 8.1 ± 2.7 days.

The comparative genomics of different microorganisms is also comm

The comparative genomics of different microorganisms is also commonly used for the screening of several candidates of interest in a short period of time. This strategy selleck products permits the genome analysis of a determined microorganism for evaluation of its proteome [16] and [17]. Recombinant technology may always

be used to improve enzymatic production, in homologous and heterologous systems, and several methods have been developed to increase recombinant protein production in fungi [18] (Figure 2). Numerous microorganisms are involved in the production of cellulases and hemicellulases, and most are filamentous fungi including Trichoderma spp. and Aspergillus spp., native or genetically modified [19] and [20••]. However, Trichoderma generally lacks β-glucosidase activity Protease Inhibitor Library concentration and Aspergillus is one of the fungi genera

most studied for production of this enzyme [21]. Thus, many studies have reported blending enzymes from these two microorganisms as a method to maximize conversion of lignocellulose to monosaccharide sugars. Recently, some studies have concentrated efforts on isolation of cellulases and hemicellulases from plant pathogenic fungi. These microorganisms produce hydrolases for plant cell wall degradation and fast invasion [22]. Therefore, some works have reported the utilization of these fungi in production of enzymes for biomass saccharification. Fungi such as Pycnoporus sanguineus [23•], Chrysoporthe cubensis Olopatadine [24•] and [25•] and Fusarium verticillioides [20••] and [26•], presented great potential for plant biomass saccharification, specially alkali pretreated sugarcane bagasse. It is already known that enzyme extracts obtained from a single microorganism are not so efficient in biomass hydrolysis, mainly because of the misbalance of enzymes. Normally cocktails have different enzymes in an adequate proportion so they are specific to individual pretreated biomass compositions. Furthermore, enzymes

need to present stability for temperature and pH ranges, resistance to product inhibition, synergism in actuation and high catalytic activity. Blending of individual enzymes and complementing crude enzyme extracts shows promise, since it can result in synergistic effects to improve biomass saccharification efficiency [25•]. Co-cultivation has often been performed to obtain improved lignocellulose hydrolysis. This technique consists in the cultivation of more than one compatible fungal species that secret hydrolytic enzymes and results in better degradation of the substrate [27]. Another alternative is enzymatic production on-site [28] and [29•]. In this case the enzymes do not need to be highly concentrated, and furthermore no accessory enzyme activity is lost in intense concentration/purification processes, which contributes to reduce the process costs.

It suggests that at the largest spatial

scales, state-of-

It suggests that at the largest spatial

scales, state-of-the-art representations of physical Ceritinib processes and assimilation approaches embedded in the reanalysis methods, while quite different among the different reanalyses, produce consistent results. In essence, this means that important variables used for ocean carbon model forcing are similar on global scales, and that whatever important differences there are among the four reanalysis products, global ocean carbon mean fluxes and pCO2 are insensitive to them. This is less sweeping when one considers that only a portion of the vast reanalysis variables produced are important in ocean carbon modeling, the most important of which are surface temperature, wind speeds and stresses, and ice distributions, and when the sensitivities of ocean carbon models are determined by complex interactions in the model formulations. Although the global carbon flux and pCO2 distributions are similar among reanalyses, there are considerable differences on oceanographic basin scales. Air–sea carbon fluxes,

which, as small differences between large values of atmospheric and ocean pCO2, are especially sensitive to small variations in the representation of atmospheric forcing by reanalysis products. None of the reanalysis products are uniformly superior in all basins, nor are any uniformly inferior, as compared to in situ estimates. The differences among the reanalyses are largest in the high latitudes and the tropics, MAPK Inhibitor Library mw which incidentally represent the basins of strongest sinks and strongest sources, respectively. Few of the

major departures observed in MERRA forcing, such as the South Atlantic and Pacific, North Flucloronide Indian, North Central Pacific, and North Pacific, are rectified by the other reanalysis products (Fig. 5). ECMWF forcing, however, substantially ameliorates the departures observed in the MERRA and NCEP forcings in the North Indian and the Equatorial Pacific. Attribution of the differences of air–sea fluxes to specific variables in the reanalysis products is difficult because of the complexity of the ocean carbon cycle. Additionally, differences in annual mean fluxes shown here can be the result of seasonal differences in reanalysis products. A complete analysis of the effects of the reanalysis products and their influences on the representation of the global ocean carbon cycle is beyond the scope of this paper. However, it is worthwhile to attempt to relate differences in forcing with differences in fluxes, at least at coarse basin and annual scales, to assist in understanding how the reanalysis variables are affecting the observed changes in the representation of the global ocean carbon cycle. First, we note that there are really only 6 reanalysis variables affecting the air–sea fluxes in this biogeochemical model: ice concentrations, SST, surface pressure, wind speeds, and the x and y components of wind stress ( Fig. 1).

This correlation was not found in eastern catchments From the fa

This correlation was not found in eastern catchments. From the factor analysis, it is concluded that the first three factors explained 47% of the variance in the dataset (Table 4). In the first factor, positive loadings consist of temperature, precipitation, artificial area and cultivated area. The negative loadings consist of shrubs and herbs, wetlands and mixed forest. These click here positive and negative

components have an inverse relationship such that the first factor explains 21% of the variance. TNC, TNL and TPC are positively correlated with the factor scores of this factor. This means that the more positive the factor scores in a catchment (explained by the positive loadings), the higher TNC, TNL and TPC will be in that catchment. The opposite is also true. The factor scores of the first factor are presented in Fig. 2a. This figure shows that the first factor is more important in the more northern catchments. The positive loadings of the second factor consist of coniferous forest, water bodies and discharge and the negative loadings consist of cultivated

area, artificial area and temperature. This relationship explains 18% of the variance. TNC, TNL, TPC and TPL are not influenced by this factor. The factor scores of the second and third factor do not show a clear pattern (Fig. 2b and c). The third factor explains 8% of the variance and consists of deciduous forest (positive) and artificial area, cultivated area and coniferous forest Ibrutinib cell line (negative). TPC is negatively correlated with this factor which means that the more positive the factor scores in a catchment (more deciduous forest), the lower TPC will be in that catchment.

The more negative second the factor scores in a catchment (more artificial area, cultivated area and coniferous forest), the higher TPC will be in that catchment. The opposite is true for TNL. The size of the catchment is not influencing any factor. The seasonal Mann–Kendall trend test revealed a sharp difference in trends for TN and TP between the east and the west of the BSDB both in loads and concentrations. In the east, trends for TNC and TNL are generally negative whereas trends for TPC and TPL are generally positive. In western catchments, more positive trends are found for the loads while more negative trends are found for the concentrations, likely because of increased discharge in the west. Since the eastern BSDB has experienced a more drastic change in the socio-economic structure and development in the period 1970–2000 (resulting in the aforementioned transition period), the difference in nutrient trends in the east suggests that the societal changes have led to significant changes in the diffuse and point sources influencing the concentrations and loads of TN and TP.