The Harvest of Beluga Whales in Canada's Western Arctic: Hunter-based Monitoring of the Size and Composition of the Catch

Hunter-based beluga monitoring programs, in place in the Mackenzie Delta since 1973 and in the Paulatuk, Northwest Territories, area since 1989, have resulted in collection of data on the number of whales harvested and on the efficiency of the hunts. Since 1980, data on the standard length, fluke width, sex, and age of the landed whales have also been collected. The number of belugas landed each year averaged 131.8 (SD 26.5, n = 1337) between 1970 and 1979, 124.0 (SD 23.3, n = 1240) between 1980 and 1989, and 111.0 (SD 19.0, n = 1110) between 1990 and 1999. The human population increased during this same period. Removal of belugas from the Beaufort Sea stock, including landed whales taken in the Alaskan harvests, is estimated at 189 per year. The sex ratio of landed belugas from the Mackenzie Estuary was 2.3 males:1 female. Median ages were 23.5 yr (47 growth layer groups [GLG]) for females (n = 80) and 24 yr (48 GLG) for males (n = 286). More than 92% of an aged sample (n = 368) from the harvest consisted of whales 10 or more years old (20 GLG). The rate of removal is small in relation to the expected maximum net productivity rate of this stock. The continued availability of large, old individuals after centuries of harvesting and the apparent lack of change in the size and age structure of the catch in recent years also support a conclusion that the present level of harvest is sustainable.Des programmes de surveillance du bélouga gérés par les chasseurs et mis en oeuvre dans le delta du Mackenzie depuis 1973 et dans la région de Paulatuk (Territoires du Nord-Ouest) depuis 1989, ont abouti à la collecte de données sur le nombre de baleines blanches prélevées et sur l'efficacité des expéditions de chasse. Depuis 1980, on a également collecté des données sur la longueur standard, la largeur de la nageoire caudale, le sexe et l'âge des bélougas ramenés à terre. Le nombre moyen de bélougas ramenés à terre chaque année était de 131,8 (écart-type 26,5, n = 1337) entre 1970 et 1979, de 124,0 (écart-type 23,3, n = 1240) entre 1980 et 1989, et de 111,0 (écart-type 19,0, n = 1110) entre 1990 et 1999. La population humaine s'est accrue durant cette même période. On estime à 189 le nombre annuel de bélougas prélevés sur le stock de la mer de Beaufort, y compris ceux ramenés à terre qui font partie des récoltes de l'Alaska. Le rapport des sexes des bélougas ramenés à terre depuis l'estuaire du Mackenzie était de 2,3 mâles pour 1 femelle. La moyenne d'âge était de 23,5 (47 groupes de couches de croissance [GCC]) pour les femelles (n = 80) et 24 (48 GCC) pour les mâles (n = 286). Plus de 92 p. cent d'un échantillonnage (n = 368) prélevé sur la récolte et dont l'âge avait été déterminé, consistait en des baleines blanches de 10 ans ou plus (20 GCC). Le taux de retrait est faible par rapport à la productivité maximale nette à laquelle on peut s'attendre de ce stock particulier. La présence continue d'individus âgés et de grande taille après des siècles de prélèvements, et le manque apparent de changements dans le nombre des prises et leur distribution d'âge au cours des dernières années permettent de conclure que le niveau actuel des prélèvements est durable

Bridging the Technology Readiness "Valley of Death" Utilizing Nanosats

Incorporating new technology is a hallmark of space missions. Missions demand ever-improving tools and techniques to allow them to meet the mission science requirements. In Earth Science, these technologies are normally expressed in new instrument capabilities that can enable new measurement concepts, extended capabilities of existing measurement techniques, or totally new detection capabilities, and also, information systems technologies that can enhance data analysis or enable new data analyses to advance modeling and prediction capabilities. Incorporating new technologies has never been easy. There is a large development step beyond demonstration in a laboratory or on an airborne platform to the eventual space environment that is sometimes referred to as the "technology valley of death." Studies have shown that non-validated technology is a primary cause of NASA and DoD mission delays and cost overruns. With the demise of the New Millennium Program within NASA, opportunities for demonstrating technologies in space have been rare. Many technologies are suitable for a flight project after only ground testing. However, some require validation in a relevant or a space flight environment, which cannot be fully tested on the ground or in airborne systems. NASA's Earth Science Technology Program has initiated a nimble program to provide a fairly rapid turn-around of space validated technologies, and thereby reducing future mission risk in incorporating new technologies. The program, called In-Space Validation of Earth Science Technology (InVEST), now has five tasks in development. Each are 3U CubeSats and they are targeted for launch opportunities in the 2016 time period. Prior to formalizing an InVEST program, the technology program office was asked to demonstrate how the program would work and what sort of technologies could benefit from space validation. Three projects were developed and launched, and have demonstrated the technologies that they set out to validate. This paper will provide a brief status of the pre-InVEST CubeSats, and discuss the development and status of the InVEST program. Figur

Belugas in the Mackenzie River estuary, NT, Canada: Habitat use and hot spots in the Tarium Niryutait Marine Protected Area

AbstractThe Tarium Niryutait MPA (TNMPA) was created in 2010, through the collaborative efforts of Fisheries and Oceans Canada, the Inuvialuit, private industry and local stakeholders. The purpose of the TNMPA is to conserve and protect the biological resources within the Mackenzie Estuary, ensuring viability of a healthy population of beluga whales. TNMPA regulations allow for the conduct of certain industry activities (e.g., dredging, transportation, and hydrocarbon exploration and production activity), as long as disturbance, damage, destruction or removal of belugas do not occur or are not expected. Our goal is to summarize baseline knowledge of the times, areas and patterns of aggregation of belugas in the TNMPA, to inform future monitoring, research and environmental assessments of any developments proposed for the TNMPA. Sightings of surfaced belugas in the Mackenzie River estuary made during seven summers of aerial surveys between 1977 and 1992 were examined using contemporary geospatial analytical methods. A total of 77 aerial surveys met the minimum criteria for inclusion: flown in their entirety, without interruption, under calm sea conditions, and with full visibility. The distribution of surfaced belugas was significantly clustered in three time periods (June 26–July 9, July 10–20, July 21–31) and in all sub areas of the TNMPA (Ripley's L, p < 0.0001). Sighting rates varied by subarea and time period, with Niaqunnaq Bay having rates 3–4 times higher (p < 0.0001) in the corresponding period, compared with West Mackenzie (WM), East Mackenzie (EM) and Kugmallit (KB) bays, in all but WM in late July. During early and mid-July of 1977–1985, belugas were aggregated in seven localized, recurrent geographic areas within the TNMPA, termed here as ‘hot spots’. Results will foster more confident and informed decisions about the acceptability of proposed industry activities in the TNMPA, ensuring assessments are evidence-based and not unnecessary restrictive

Detection of Mutated K- ras DNA in Urine, Plasma, and Serum of Patients with Colorectal Carcinoma or Adenomatous Polyps

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72277/1/annals.1448.027.pd

Summer Distribution of Bowhead Whales, Balaena mysticetus, Relative to Oil Industry Activities in the Canadian Beaufort Sea, 1980-84

Aerial surveys in 1980-84 showed that summer distribution of bowheads in the Beaufort Sea varied markedly between years. Distribution varied both outside and within the "main industrial area" (MIA), the area of island construction, drilling and intensive ship and helicopter traffic. Numbers of bowheads in the MIA were high in 1980, lower in 1981, near zero in 1982 and very low in 1983-84. The few whales in the MIA in 1983-84 were mainly near its edges, contrary to 1980-81. These data, plus limited evidence from 1976-79, indicate that bowheads were numerous in the centre of the MIA in 3 of 5 years from 1976-80 (1976-77, 1980) vs. 0 of 4 years from 1981-84. One hypothesis is that progressively increasing industrial activities affected bowhead distribution after 1980. However, bowheads probably also react to variations in their zooplankton prey, which may be affected by year-to-year changes in oceanography and weather. Influences of natural factors on zooplankton and bowheads need to be better understood in order to assess whether oil exploration caused any of the observed changes in bowhead distribution.Key words: bowhead whale, Balaena mysticetus, Beaufort Sea, oil exploration, seismic exploration, aerial surveysDes relev&eacute;s a&eacute;riens effectu&eacute;s entre 1980 et 1984 ont montr&eacute; que la r&eacute;partition estivale des baleines franches dans la mer de Beaufort varie nettement d&rsquo;ann&eacute;e en ann&eacute;e. Elle varie &agrave; fois &agrave; l&rsquo;ext&eacute;rieure et &agrave; l&rsquo;int&eacute;rieur de la &ldquo;zone industrielle principale&rdquo; (ZIP), qui est la zone de construction de l&rsquo;&icirc;le, de forage et de circulation intense de bateaux et d&rsquo;h&eacute;licopt&egrave;res. Le nombre de baleines franches dans la ZIP &eacute;tait &eacute;lev&eacute; en 1980, plus bas en 1981, pr&egrave;s de z&eacute;ro en 1982, et tr&egrave;s bas en 1983 et 1984. Les quelques baleines pr&eacute;sentes dans la ZIP en 1983 et 1984 &eacute;taient principalement &agrave; la p&eacute;riph&eacute;rie, contrairement &agrave; 1980 et 1981. Ces donn&eacute;es, jointes &agrave; des &eacute;vidences plus limit&eacute;es de 1976 &agrave; 1979, indiquent que les baleines franches &eacute;taient nombreuses au centre de la ZIP pendant 3 ann&eacute;es sur 5, allant de 1976 &agrave; 1980 (1976, 1977 et 1980), par rapport &agrave; aucune ann&eacute;e sur les quatre allant de 1981 &agrave; 1984. On avance l&rsquo;hypoth&egrave;se que les activit&eacute;s industrielles progressivement croissantes ont affect&eacute; la r&eacute;partition des baleines franches apr&egrave;s 1980. Cependant, les baleines franches ont probablement r&eacute;agi aussi aux variations de zooplancton qui constitue leur nourriture et qui peut &ecirc;tre affect&eacute; par les changements qui ont lieu d&rsquo;ann&eacute;e en ann&eacute;e dans l&rsquo;oc&eacute;anographie et le climat. I1 est n&eacute;cessaire de mieux comprendre l&rsquo;influence des facteurs naturels sur le zooplancton et les baleines franches afin d&rsquo;&eacute;valuer si l&rsquo;exploration p&eacute;troli&egrave;re a provoqu&eacute; l&rsquo;un quelconque des changements observ&eacute;s dans la r&eacute;partition de ces baleines.Mots cl&eacute;s: baleine franche, Balaena mysticetus, mer de Beaufort, exploration p&eacute;troli&egrave;re, exploration sismique, relev&eacute;s a&eacute;rien

Predictive factor for the response to adjuvant therapy with emphasis in breast cancer

One of the major challenges of early-stage breast cancer is to select the adjuvant therapy that ensures the most benefits and the least harm for the patient. The definition of accurate predictive factors is therefore of paramount importance. So far the choice of adjuvant therapy has been based on the number of affected lymph nodes and the hormone receptor status of the patient. This paper evaluates the use of other tumor-related markers as predictive factors for adjuvant therapy. These include HER2, p53 and Bcl-2, cathepsin B, p27, proliferating cell nuclear antigen (PCNA), cyclin D, Ki-67, and vascular endothelial growth factor (VEGF)

Linkage Specific Fucosylation of Alpha-1-Antitrypsin in Liver Cirrhosis and Cancer Patients: Implications for a Biomarker of Hepatocellular Carcinoma

We previously reported increased levels of protein-linked fucosylation with the development of liver cancer and identified many of the proteins containing the altered glycan structures. One such protein is alpha-1-antitrypsin (A1AT). To advance these studies, we performed N-linked glycan analysis on the five major isoforms of A1AT and completed a comprehensive study of the glycosylation of A1AT found in healthy controls, patients with hepatitis C- (HCV) induced liver cirrhosis, and in patients infected with HCV with a diagnosis of hepatocellular carcinoma (HCC).Patients with liver cirrhosis and liver cancer had increased levels of triantennary glycan-containing outer arm (alpha-1,3) fucosylation. Increases in core (alpha-1,6) fucosylation were observed only on A1AT from patients with cancer. We performed a lectin fluorophore-linked immunosorbent assay using Aleuria Aurantia lectin (AAL), specific for core and outer arm fucosylation in over 400 patients with liver disease. AAL-reactive A1AT was able to detect HCC with a sensitivity of 70% and a specificity of 86%, which was greater than that observed with the current marker of HCC, alpha-fetoprotein. Glycosylation analysis of the false positives was performed; results indicated that these patients had increases in outer arm fucosylation but not in core fucosylation, suggesting that core fucosylation is cancer specific.This report details the stepwise change in the glycosylation of A1AT with the progression from liver cirrhosis to cancer and identifies core fucosylation on A1AT as an HCC specific modification

Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely