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Article Authors Metrics Comments Media Coverage Peer Review Abstract Introduction Statistical method Illustrative examples Discussion Supporting information Acknowledgments References Reader Comments Figures Abstract Working groups for integrated ecosystem assessments are often challenged with understanding and assessing recent change in ecosystems. As a basis for this, the groups typically have at their disposal many time series and will often need to prioritize which ones to follow up for closer analyses and assessment. In this article we provide a procedure termed Flagged Observation analysis that can be applied to all the available time series to help identifying time series that should be prioritized. The statistical procedure first applies a structural time series model including a stochastic trend model to the data to estimate the long-term trend. The model adopts a state space representation, and the trend component is estimated by a Kalman filter algorithm. The algorithm obtains one- or more-years-ahead prediction values using all past information from the data. Thus, depending on the number of years the investigator wants to consider as “the most recent”, the expected trend for these years is estimated through the statistical procedure by using only information from the years prior to them. Forecast bands are estimated around the predicted trends for the recent years, and in the final step, an assessment is made on the extent to which observations from the most recent years fall outside these forecast bands. Those that do, may be identified as flagged observations. A procedure is also presented for assessing whether the combined information from all the most recent observations form a pattern that deviates from the predicted trend and thus represents an unexpected tendency that may be flagged. In addition to form the basis for identifying time series that should be prioritized in an integrated ecosystem assessment, flagged observations can provide the basis for communicating with managers and stakeholders about recent ecosystem change. Applications of the framework are illustrated with two worked examples.

Copy number aberrations frequently occur during the development of many cancers. Such events affect dosage of involved genes and may cause further genomic instability and progression of cancer. In this survey, canine SNP microarrays were used to study 117 canine mammary tumours from 69 dogs. We found a high occurrence of copy number aberrations in canine mammary tumours, losses being more frequent than gains. Increased frequency of aberrations and loss of heterozygosity were positively correlated with increased malignancy in terms of histopathological diagnosis. One of the most highly recurrently amplified regions harbored the MYC gene. PTEN was located to a frequently lost region and also homozygously deleted in five tumours. Thus, deregulation of these genes due to copy number aberrations appears to be an important event in canine mammary tumour development. Other potential contributors to canine mammary tumour pathogenesis are COL9A3, INPP5A, CYP2E1 and RB1. The present study also shows that a more detailed analysis of chromosomal aberrations associated with histopathological parameters may aid in identifying specific genes associated with canine mammary tumour progression. The high frequency of copy number aberrations is a prominent feature of canine mammary tumours as seen in other canine and human cancers. Our findings share several features with corresponding studies in human breast tumours and strengthen the dog as a suitable model organism for this disease.

This paper provides a statistical methodology for quantifying causality in complex dynamical systems, based on analysis of multidimensional time series data of the state variables. The methodology integrates Granger’s causality analysis based on the log-likelihood function expansion (Partial pair-wise causality), and Akaike’s power contribution approach over the whole frequency domain (Total causality). The proposed methodology addresses a major drawback of existing methodologies namely, their inability to use time series observation of state variables to quantify causality in complex systems. We first perform a simulation study to verify the efficacy of the methodology using data generated by several multivariate autoregressive processes, and its sensitivity to data sample size. We demonstrate application of the methodology to real data by deriving inter-species relationships that define key food web drivers of the Barents Sea ecosystem. Our results show that the proposed methodology is a useful tool in early stage causality analysis of complex feedback systems.

Inferring the number of genetically distinct populations and their levels of connectivity is of key importance for the sustainable management and conservation of wildlife. This represents an extra challenge in the marine environment where there are few physical barriers to gene-flow, and populations may overlap in time and space. Several studies have investigated the population genetic structure within the North Atlantic minke whale with contrasting results. In order to address this issue, we analyzed ten microsatellite loci and 331 bp of the mitochondrial D-loop on 2990 whales sampled in the North East Atlantic in the period 2004 and 2007–2011. The primary findings were: (1) No spatial or temporal genetic differentiations were observed for either class of genetic marker. (2) mtDNA identified three distinct mitochondrial lineages without any underlying geographical pattern. (3) Nuclear markers showed evidence of a single panmictic population in the NE Atlantic according STRUCTURE's highest average likelihood found at K = 1. (4) When K = 2 was accepted, based on the Evanno's test, whales were divided into two more or less equally sized groups that showed significant genetic differentiation between them but without any sign of underlying geographic pattern. However, mtDNA for these individuals did not corroborate the differentiation. (5) In order to further evaluate the potential for cryptic structuring, a set of 100 in silico generated panmictic populations was examined using the same procedures as above showing genetic differentiation between two artificially divided groups, similar to the aforementioned observations. This demonstrates that clustering methods may spuriously reveal cryptic genetic structure. Based upon these data, we find no evidence to support the existence of spatial or cryptic population genetic structure of minke whales within the NE Atlantic. However, in order to conclusively evaluate population structure within this highly mobile species, more markers will be required.

Granger [14] followed this notion of causality, and applied it to the analysis of economic time series data. 14. The AR order is identified by statistical model selection approaches, such as, the Akaike Information Criterion (AIC) [17], defined by(2) 17. The correct sentence should read: Since MAR model includes full coefficients, the model that the abiotic factor is treated as an exogeneous variable seen in [3, 26] could capture more significant relationship between capelin and herring. 3.

Recent warming in the Barents Sea has led to changes in the spatial distribution of both zooplankton and fish, with boreal communities expanding northwards. A similar northward expansion has been observed in several rorqual species that migrate into northern waters to take advantage of high summer productivity, hence feeding opportunities. Based on ecosystem surveys conducted during August–September in 2014–2017, we investigated the spatial associations among the three rorqual species of blue, fin, and common minke whales, the predatory fish Atlantic cod, and their main prey groups (zooplankton, 0-group fish, Atlantic cod, and capelin) in Arctic Ocean waters to the west and north of Svalbard. During the surveys, whale sightings were recorded by dedicated whale observers on the bridge of the vessel, whereas the distribution and abundance of cod and prey species were assessed using trawling and acoustic methods. Based on existing knowledge on the dive habits of these rorquals, we divided our analyses into two depth regions: the upper 200 m of the water column and waters below 200 m. Since humpback whales were absent in the area in 2016 and 2017, they were not included in the subsequent analyses of spatial association. No association or spatial overlap between fin and blue whales and any of the prey species investigated was found, while associations and overlaps were found between minke whales and zooplankton/0-group fish in the upper 200 m and between minke whales and Atlantic cod at depths below 200 m. A prey detection range of more than 10 km was suggested for minke whales in the upper water layers.

Background: In the marine environment, where there are few absolute physical barriers, contemporary contact between previously isolated species can occur across great distances, and in some cases, may be inter-oceanic. An example of this can be seen in the minke whale species complex. Antarctic minke whales are genetically and morphologically distinct from the common minke found in the north Atlantic and Pacific oceans, and the two species are estimated to have been isolated from each other for 5 million years or more. Recent atypical migrations from the southern to the northern hemisphere have been documented and fertile hybrids and back-crossed individuals between both species have also been identified. However, it is not known whether this represents a contemporary event, potentially driven by ecosystem changes in the Antarctic, or a sporadic occurrence happening over an evolutionary time-scale. We successfully used whole genome resequencing to identify a panel of diagnostic SNPs which now enable us address this evolutionary question. Results: A large number of SNPs displaying fixed or nearly fixed allele frequency differences among the minke whale species were identified from the sequence data. Five panels of putatively diagnostic markers were established on a genotyping platform for validation of allele frequencies; two panels (26 and 24 SNPs) separating the two species of minke whale, and three panels (22, 23, and 24 SNPs) differentiating the three subspecies of common minke whale. The panels were validated against a set of reference samples, demonstrating the ability to accurately identify back-crossed whales up to three generations. Conclusions: This work has resulted in the development of a panel of novel diagnostic genetic markers to address inter-oceanic and global contact among the genetically isolated minke whale species and sub-species. These markers, including a globally relevant genetic reference data set for this species complex, are now openly available for researchers interested in identifying other potential whale hybrids in the world's oceans. The approach used here, combining whole genome resequencing and high-throughput genotyping, represents a universal approach to develop similar tools for other species and population complexes.

Premise of the study The apical meristem generates indeterminate apical growth of the stem and root of vascular plants. Our previous examination showed that shoot apical meristems (SAMs) can be classified into two types based on plasmodesmatal networks (PNs), which are important elements in symplasmic signaling pathways within the apical meristem. Here, we examined the PNs of root apical meristems (RAMs) in comparison with those of SAMs. Methods Root apical meristems of 18 families and 22 species of lycophytes and euphyllophytes were analyzed. Plasmodesmata (PD) in cell walls in median longitudinal sections of RAMs were enumerated using transmission electron micrographs, and the PD density per 1 μm2 of each cell wall was calculated. Key results Root apical meristems with prominent apical cells of monilophytes (euphyllophytes) and Selaginellaceae (lycophytes) had high PD densities, while RAMs with plural initial cells of gymnosperms and angiosperms (euphyllophytes), and of Lycopodiaceae and Isoetaceae (lycophytes) had low PD densities. This correlation between structures of apical meristems and PD densities is identical to that in SAMs already described. Conclusions Irrespective of their diversified structures, the RAMs of vascular plants can be classified into two types with respect to PNs: the fern (monilophyte) type, which has a lineage‐specific PN with only primary PD, and the seed‐plant type, which has an interspecific PN with secondary PD in addition to primary PD. PNs may have played a key role in the evolution of apical meristems in vascular plants.

The common minke whale ( Balaenoptera acutorostrata acutorostrata ) is a boreo-arctic species, and the summer period is generally characterized by intensive feeding and consequently seasonal fattening at high latitudes. The fat deposited is stored as energy reserves for overwintering at lower latitudes where feeding is greatly reduced. It is therefore expected that their body condition on the summer grounds will reflect food availability during their most intensive feeding period and thus indicate how well the high-latitude ecosystems can support the populations. During the commercial catch operations on feeding grounds in Norwegian waters, body condition data (blubber thickness and girth) were collected from 10,556 common minke whales caught from 1993 to 2013. To investigate associations between condition and time/area, we applied the following three models: (1) multiple regression models with covariates, sex, year, latitude and longitude, to find significant coefficients of the covariate; (2) random effect models involving the random effects of variations by year or area and with sex as a fixed variable; (3) varying coefficient models, which were applied to investigate variation with year/area and to interpret covariate effects by visualizations. The significance of the estimated coefficients can be assessed by statistical tests. In conclusion, the total trend over the 2 decades of data available suggests a decrease in minke whale condition. However, this trend was most pronounced during the high summer season when we considered the seasonal effect over the annual sampling periods from April to September.

The majority of human breast cancers exhibit luminal epithelial differentiation. However, most aggressive behavior, including invasion and purported cancer stem cell activity, are considered characteristics of basal-like cells. We asked the following questions: Must luminal-like breast cancer cells become basal-like to initiate tumors or to invade? Could luminally differentiated cells within a basally initiated hierarchy also be tumorigenic? To answer these questions, we used rare and mutually exclusive lineage markers to isolate subsets of luminal-like and basal-like cells from human breast tumors. We enriched for populations with or without prominent basal-like traits from individual tumors or single cell cloning from cell lines and recovered cells with a luminal-like phenotype. Tumor cells with basal-like traits mimicked phenotypic and functional behavior associated with stem cells assessed by gene expression, mammosphere formation and lineage markers. Luminal-like cells without basal-like traits, surprisingly, were fully capable of initiating invasive tumors in NOD SCID gamma (NSG) mice. In fact, these phenotypically pure luminal-like cells generated larger and more invasive tumors than their basal-like counterparts. The tumorigenicity and invasive potential of the luminal-like cancer cells relied strongly on the expression of the gene GCNT1, which encodes a key glycosyltransferase controlling O-glycan branching. These findings demonstrate that basal-like cells, as defined currently, are not a requirement for breast tumor aggressiveness, and that within a single tumor there are multiple \"stem-like\" cells with tumorigenic potential casting some doubt on the hypothesis of hierarchical or differentiative loss of tumorigenicity.