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Past model studies have projected a global decrease in marine net primary production (NPP) over the 21st century, but these studies focused on the multi-model mean rather than on the large inter-model differences. Here, we analyze model-simulated changes in NPP for the 21st century under IPCC's high-emission scenario RCP8.5. We use a suite of nine coupled carbon–climate Earth system models with embedded marine ecosystem models and focus on the spread between the different models and the underlying reasons. Globally, NPP decreases in five out of the nine models over the course of the 21st century, while three show no significant trend and one even simulates an increase. The largest model spread occurs in the low latitudes (between 30° S and 30° N), with individual models simulating relative changes between −25 and +40 %. Of the seven models diagnosing a net decrease in NPP in the low latitudes, only three simulate this to be a consequence of the classical interpretation, i.e., a stronger nutrient limitation due to increased stratification leading to reduced phytoplankton growth. In the other four, warming-induced increases in phytoplankton growth outbalance the stronger nutrient limitation. However, temperature-driven increases in grazing and other loss processes cause a net decrease in phytoplankton biomass and reduce NPP despite higher growth rates. One model projects a strong increase in NPP in the low latitudes, caused by an intensification of the microbial loop, while NPP in the remaining model changes by less than 0.5 %. While models consistently project increases NPP in the Southern Ocean, the regional inter-model range is also very substantial. In most models, this increase in NPP is driven by temperature, but it is also modulated by changes in light, macronutrients and iron as well as grazing. Overall, current projections of future changes in global marine NPP are subject to large uncertainties and necessitate a dedicated and sustained effort to improve the models and the concepts and data that guide their development.

Error-quantified, synoptic-scale relationships between chlorophyll-a (Chl-a) and phytoplankton pigment groups at the sea surface are presented. A total of ten pigment groups were considered to represent three Phytoplankton Size Classes (PSCs, micro-, nano- and picoplankton) and seven Phytoplankton Functional Types (PFTs, i.e. diatoms, dinoflagellates, green algae, prymnesiophytes (haptophytes), pico-eukaryotes, prokaryotes and Prochlorococcus sp.). The observed relationships between Chl-a and PSCs/PFTs were well-defined at the global scale to show that a community shift of phytoplankton at the basin and global scales is reflected by a change in Chl-a of the total community. Thus, Chl-a of the total community can be used as an index of not only phytoplankton biomass but also of their community structure. Within these relationships, we also found non-monotonic variations with Chl-a for certain pico-sized phytoplankton (pico-eukaryotes, Prokaryotes and Prochlorococcus sp.) and nano-sized phytoplankton (Green algae, prymnesiophytes). The relationships were quantified with a least-square fitting approach in order to enable an estimation of the PFTs from Chl-a where PFTs are expressed as a percentage of the total Chl-a. The estimated uncertainty of the relationships depends on both PFT and Chl-a concentration. Maximum uncertainty of 31.8% was found for diatoms at Chl-a = 0.49 mg m−3. However, the mean uncertainty of the relationships over all PFTs was 5.9% over the entire Chl-a range observed in situ (0.02 < Chl-a < 4.26 mg m−3). The relationships were applied to SeaWiFS satellite Chl-a data from 1998 to 2009 to show the global climatological fields of the surface distribution of PFTs. Results show that microplankton are present in the mid and high latitudes, constituting only ~10.9% of the entire phytoplankton community in the mean field for 1998–2009, in which diatoms explain ~7.5%. Nanoplankton are ubiquitous throughout the global surface oceans, except the subtropical gyres, constituting ~45.5%, of which prymnesiophytes (haptophytes) are the major group explaining ~31.7% while green algae contribute ~13.9%. Picoplankton are dominant in the subtropical gyres, but constitute ~43.6% globally, of which prokaryotes are the major group explaining ~26.5% (Prochlorococcus sp. explaining 22.8%), while pico-eukaryotes explain ~17.2% and are relatively abundant in the South Pacific. These results may be of use to evaluate global marine ecosystem models.

Morphological operators are nonlinear transformations commonly used in image processing. Their theoretical foundation is based on lattice theory, and it is a well-known result that a large class of image operators can be expressed in terms of two basic ones, the erosions and the dilations. In practice, useful operators can be built by combining these two operators, and the new operators can be further combined to implement more complex transformations. The possibility of implementing a compact combination that performs a complex transformation of images is particularly appealing in resource-constrained hardware scenarios. However, finding a proper combination may require a considerable trial-and-error effort. This difficulty has motivated the development of machine-learning-based approaches for designing morphological image operators. In this work, we present an overview of this topic, divided in three parts. First, we review and discuss the representation structure of morphological image operators. Then we address the problem of learning morphological image operators from data, and how representation manifests in the formulation of this problem as well as in the learned operators. In the last part we focus on recent morphological image operator learning methods that take advantage of deep-learning frameworks. We close with discussions and a list of prospective future research directions.

We investigated the mechanisms of phytoplankton competition during the spring bloom, one of the most dramatic seasonal events in lower-trophic-level ecosystems, in four state-of-the-art plankton functional type (PFT) models: PISCES, NEMURO, PlankTOM5 and CCSM-BEC. In particular, we investigated the relative importance of different ecophysiological processes on the determination of the community structure, focusing both on the bottom-up and the top-down controls. The models reasonably reproduced the observed global distribution and seasonal variation of phytoplankton biomass. The fraction of diatoms with respect to the total phytoplankton biomass increases with the magnitude of the spring bloom in all models. However, the governing mechanisms differ between models, despite the fact that current PFT models represent ecophysiological processes using the same types of parameterizations. The increasing trend in the percentage of diatoms with increasing bloom magnitude is mainly caused by a stronger nutrient dependence of diatom growth compared to nanophytoplankton (bottom-up control). The difference in the maximum growth rate plays an important role in NEMURO and PlankTOM5 and determines the absolute values of the percentage of diatoms during the bloom. In CCSM-BEC, the light dependency of growth plays an important role in the North Atlantic and the Southern Ocean. The grazing pressure by zooplankton (top-down control), however, strongly contributes to the dominance of diatoms in PISCES and CCSM-BEC. The regional differences in the percentage of diatoms in PlankTOM5 are mainly determined by top-down control. These differences in the mechanisms suggest that the response of marine ecosystems to climate change could significantly differ among models, even if the present-day ecosystem is reproduced to a similar degree of confidence. For further understanding of plankton competition and for the prediction of future change in marine ecosystems, it is important to understand the relative differences in each physiological rate and life history rate in the bottom-up and the top-down controls between PFTs.

Errors in measurements are key to weighting the value of data, but are often neglected in machine learning (ML). We show how convolutional neural networks (CNNs) are able to learn about the context and patterns of signal and noise, leading to improvements in the performance of classification methods. We construct a model whereby two classes of objects follow an underlying Gaussian distribution, and where the features (the input data) have varying, but known, levels of noise—in other words, each data point has a different error bar. This model mimics the nature of scientific data sets, such as those from astrophysical surveys, where noise arises as a realization of random processes with known underlying distributions. The classification of these objects can then be performed using standard statistical techniques (e.g. least squares minimization), as well as ML techniques. This allows us to take advantage of a maximum likelihood approach to object classification, and to measure the amount by which the ML methods are incorporating the information in the input data uncertainties. We show that, when each data point is subject to different levels of noise (i.e. noises with different distribution functions, which is typically the case in scientific data sets), that information can be learned by the CNNs, raising the ML performance to at least the same level of the least squares method—and sometimes even surpassing it. Furthermore, we show that, with varying noise levels, the confidence of the ML classifiers serves as a proxy for the underlying cumulative distribution function, but only if the information about specific input data uncertainties is provided to the CNNs.

Background: To evaluate the impact of change in the hormone receptor (HR) status (HR status conversion) on the long-term outcomes of breast cancer patients treated with neoadjuvant chemotherapy (NAC). Methods: We investigated 368 patients for the HR status of their lesions before and after NAC. On the basis of the HR status and the use/non-use of endocrine therapy (ET), the patients were categorised into four groups: Group A, 184 ET-administered patients with HR-positive both before and after NAC; Group B, 47 ET-administered patients with HR status conversion; Group C, 12 ET-naive patients with HR status conversion; Group D, 125 patients with HR-negative both before and after NAC. Results: Disease-free survival in Group B was similar to that in Group A (hazard ratio, 1.16; P =0.652), but that in Group C was significantly lesser than that in Group A (hazard ratio, 6.88; P <0.001). A similar pattern of results was obtained for overall survival. Conclusion: Our results indicate that the HR status of tumours is a predictive factor for disease-free and overall survival and that ET appears to be suitable for patients with HR status conversion. Therefore, both the CNB and surgical specimens should be monitored for HR status.

Modeling and monitoring plankton functional types (PFTs) is challenged by the insufficient amount of field measurements of ground truths in both plankton models and bio-optical algorithms. In this study, we combine remote sensing data and a dynamic plankton model to simulate an ecologically sound spatial and temporal distribution of phyto-PFTs. We apply an innovative ecological indicator approach to modeling PFTs and focus on resolving the question of diatom–coccolithophore coexistence in the subpolar high-nitrate and low-chlorophyll regions. We choose an artificial neural network as our modeling framework because it has the potential to interpret complex nonlinear interactions governing complex adaptive systems, of which marine ecosystems are a prime example. Using ecological indicators that fulfill the criteria of measurability, sensitivity and specificity, we demonstrate that our diagnostic model correctly interprets some basic ecological rules similar to ones emerging from dynamic models. Our time series highlight a dynamic phyto-PFT community composition in all high-latitude areas and indicate seasonal coexistence of diatoms and coccolithophores. This observation, though consistent with in situ and remote sensing measurements, has so far not been captured by state-of-the-art dynamic models, which struggle to resolve this \"paradox of the plankton\". We conclude that an ecological indicator approach is useful for ecological modeling of phytoplankton and potentially higher trophic levels. Finally, we speculate that it could serve as a powerful tool in advancing ecosystem-based management of marine resources.

An adenovirus vector carrying the human Reduced Expression in Immortalized Cell ( REIC )/ Dkk-3 gene (Ad-REIC) mediates simultaneous induction of cancer-selective apoptosis and augmentation of anticancer immunity. In our preclinical and clinical studies, in situ Ad-REIC gene therapy showed remarkable direct and indirect antitumor effects to realize therapeutic cancer vaccines. We herein aimed to confirm the induction of tumor-associated antigen-specific cytotoxic T lymphocytes (CTLs) by Ad-REIC. Using an ovalbumin (OVA), a tumor-associated antigen, expressing E.G7 tumor-bearing mouse model, we investigated the induction and expansion of OVA-specific CTLs responsible for indirect, systemic effects of Ad-REIC. The intratumoral administration of Ad-REIC mediated clear antitumor effects with the accumulation of OVA-specific CTLs in the tumor tissues and spleen. The CD86-positive dendritic cells (DCs) were upregulated in the tumor draining lymph nodes of Ad-REIC-treated mice. In a dual tumor-bearing mouse model in the left and right back, Ad-REIC injection in one side significantly suppressed the tumor growth on both sides and significant infiltration of OVA-specific CTLs into non-injected tumor was also detected. Consequently, in situ Ad-REIC gene therapy is expected to realize a new-generation cancer vaccine via anticancer immune activation with DC and tumor antigen-specific CTL expansion.

Objective: Long-term administration of dienogest, which is known to have effect on bone mineral density, is frequently done in patients with endometriosis and adenomyosis, but a few studies focused on the bone mineral density changes after finishing the long-term therapy. This study aimed to reveal the factors that adversely affect lumbar bone mineral density. Method: Fifty-seven premenopausal women who visited our hospital were diagnosed as either endometriosis or adenomyosis, and they were treated by dienogest for more than 115 weeks (26.5 months). Based on a previous report, bone mineral density changes less than 2% was categorized as the osteopenic group (n = 30), and the others were assigned to the unchanged group (n = 27). Bone mineral density was measured at the lumbar spine using dual-energy X-ray absorptiometry. A representative ovarian reserve marker, endogenous estradiol levels, and follicle-stimulating hormone levels were measured over time and were compared between the osteopenic and unchanged groups. Result: Duration of dienogest intake was 59.5 months (osteopenic group) versus 57.5 months (unchanged group). These patients experienced ovarian surgeries in a similar frequency, but the ovarian reserve in osteopenic group was impaired as suggested by the decline of endogenous estradiol level during intake of dienogest compared to that of unchanged group (p = 0.0146). Endogenous follicle-stimulating hormone level between osteopenic group and unchanged group did not reach statistically significant difference, although the osteopenic group showed relatively higher level. Conclusion: This study might suggest that decreased ovarian reserve as judged by endogenous estradiol level is a factor that negatively affect bone mineral density, and measurement of endogenous estradiol level during intake of dienogest could have a predictive meaning of future decreased bone mineral density level.