# Ã±os canario

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## Canário – Wikipédia, a enciclopédia livre

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From: pt.wikipedia.org

O canario[1] (Serinus canaria) e um pequeno passaro canoro, membro da familia Fringillidae. Este passaro e originario dos Acores, da ilha da Madeira e das ilhas Canarias. O seu nome vem destas ultimas, sendo que o nome das ilhas vem da palavra em latim canaria, que significa "dos caes", ja que os romanos encontraram ali muitos caes selvagens.

Nomes comuns[editar | editar codigo-fonte]

Na maioria dos paises lusofonos, da ainda pelo nome comum de canario-da-terra[2].

No Brasil, e tambem conhecido como canario-do-reino[3] ou canario-belga[4]. O nome canario-do-reino foi-lhe dado no Brasil, por oposicao ao canario-da-terra-brasileiro (Sicalis flaveola brasiliense), ave nativa desse territorio.

Historico[editar | editar codigo-fonte]

No ano de 1042, nas Ilhas Canarias, foram encontrados os primeiros canarios. Apos a ocupacao da ilha pelos espanhois, em 1478, foi que ficou conhecida a docilidade da especie, e que era possivel cria-los em cativeiro. Porem, foram os Monges que obtiveram sucesso na criacao dos passaros. A venda dos canarios era realizada somente pelos espanhois, para evitar que outras pessoas reproduzisse o passaro, apenas os machos eram vendidos. Isso acabou somente quando um navio carregado de canarios naufragou, em 1662, e os tripulantes soltaram os passaros que se espalharam por toda a Europa, encerrando assim o monopolio espanhol e dando inicio a mutacoes da especie, como o Canela, e o Roller.

Caracteristicas[editar | editar codigo-fonte]

E um passaro com um comprimento total de 12,5 centimetros e com um comprimento de asa de 71 milimetros. A sua plumagem e geralmente amarelada com a parte inferior do ventre de cor clara.

As femeas tem uma coloracao semelhante, mas mais acinzentada e menos brilhante.

O acasalamento ocorre entre Marco e Junho dependendo das condicoes atmosfericas e a postura e de quatro a cinco ovos que tem um periodo de incubacao de 15 dias. O ninho colocado, geralmente, a entre 4 e 6 metros do solo entre ramos de loureiros, pinheiros e grandes tojos arboreos, e confeccionado com fibras vegetais, ervas e folhas de estevas. Aparece muitas vezes atapetado por liquenes, pelos e penas. O macho nao colabora na incubacao mas quando os juvenis nascem e solicitado a procurar alimento. Os juvenis com tres semanas de idade sao ja capazes de voar, permanecendo ainda um certo tempo na tutela materna.

No ramo da arte da criacao de canarios, ha tres grandes grupos:

Os canarios de cor, pela classificacao da OBJO (Ordem Brasileira de Juizes de Ornitologia)[5], contam hoje com quase 450 cores.

Segundo o Manual de Julgamento dos Canarios de Porte da FOB [6](Federacao Ornitologica Brasileira)/OBJO sao cinco os grupos de porte, totalizando quarenta e tres racas.

Criacao[7][editar | editar codigo-fonte]

No inicio da criacao de canarios, e importante que o criador crie apenas uma linha de cor. E muito importante tambem associar-se a um clube ornitologico, para que haja interacao com outros criadores, acompanhamento, orientacoes, conhecer outras experiencias, etc. Na hora da escolha dos passaros deve-se ter em mente passaros jovens e saudaveis.

Devem ser observadas as pernas, os pes, a regiao ao redor do bico e narinas, se elas estao completamente limpas e lisas, livres de qualquer infestacao. De preferencia por canarios que tenham sido anilhados. A anilha e como se fosse o registro da ave, a certidao de nascimento. E um pequeno anel metalico , contendo alguns dados sobre a ave, principalmente o ano de nascimento. Preferencialmente, tente adquirir passaros do ano corrente ou, no maximo, do ano anterior. No caso de passaros adultos, procure aquele que tenha somente duas chocas.

Os meses para uma boa compra de passaros sao os meses de marco e abril, quando o criador tem uma maior possibilidade de escolha. Neste periodo, nunca deixe para ultima hora. Ao iniciar uma criacao, adquira, no maximo, cinco casais.

Alimentacao[editar | editar codigo-fonte]

Os canarios sao animais granivoros, ou seja, alimenta-se de graos e sementes que encontram em seu habitat. Criadores de canarios costumam alimenta-los com misturas, que podem ser encontradas em comercios ou feitas em casa, utilizando sementes de alta qualidade, como: alpiste, linhaca, semente de rabanete, semente de alface, semente de endivia, aveia, semente de canhamo, negrillo. Esses passaros tambem podem se alimentar de vegetais e fruta seca, que sao muito importante para fornece-los uma grande quantidade de vitaminas. Durante a epoca de reproducao e necessario adicionar calcio a alimentacao, esse nutriente pode ser encontrado em osso de siba e conchas de ostra moida.

Novas cores de canario introduzidas por cruzamentos[editar | editar codigo-fonte]

A cor vermelha foi introduzida no canario domestico[8] pelo cruzamento com o Pintassilgo-da-venezuela (Spinus cucullatus ou Carduelis cucullata), tambem chamado Tarim ou Pintassilgo-vermelho-da-America-do-Sul. O canario negro verdadeiro ainda nao existe, mas esta sendo tentado e poderia ser obtido pelo cruzamento do canario com o pintassilgo negro Carduelis atratus, conforme artigo do ornitologo Giorgio de Baseggio.[9]

Canario-da-Terra[editar | editar codigo-fonte]

Na America do Sul ha um canario nativo, chamado canario-terra ou canario-da-terra-brasileiro (Sicalis flaveola brasiliense). Nao e da mesma especie do canario Serinus canaria, tendo obtido este nome por sua aparencia e para fazer uma contraposicao ao canario que vinha de fora. Assim tem-se o "canario-da-terra" (Sicalis flaveola brasiliense) e o "canario-do-Reino" (Serinus canaria). Como especie nativa, a criacao em cativeiro do canario-da-terra depende de autorizacao do IBAMA, e sua captura na natureza constitui crime ambiental.

Referencias Ligacoes externas[editar | editar codigo-fonte]

## Luis Alberto “canario” Pereyra Un SeÃ±or

Apr 03, 2009 · Como lo dice el tÃ­tulo LUIS ALBERTO “CANARIO” PEREYRA UN SEÃ‘OR CON TODAS LAS LETRAS, se merece mucho mÃ¡s de lo que tiene. Tengo la suerte de conocerlo y saber de que persona se trata, una humildad a toda prueba, es una lÃ¡stima no pueda contar cosas que lo pintarÃ­an de cuerpo y alma como es Ã©ste TIPO..
From: carnavaldeluruguay.com

Como lo dice el tA­tulo LUIS ALBERTO «CANARIO» PEREYRA UN SEA‘OR CON TODAS LAS LETRAS, se merece mucho mA¡s de lo que tiene.
Tengo la suerte de conocerlo y saber de que persona se trata, una humildad a toda prueba, es una lA¡stima no pueda contar cosas que lo pintarA­an de cuerpo y alma como es A©ste TIPO.
MI IDOLO DE TODA LA VIDA, lo era por sus cualidades artA­sticas, pero cuando lleguA© a conocerlo personalmente MI IDOLO DE LA VIDA.

OJALA TENGA LA SUERTE DE GANAR LA MENCION DE LA MEJOR VOZ DEL CARNAVAL

LES CUENTO QUE ESTOY CREANDO UNA PAGINA DE ESTE HUMILDE PERSONAJE SI QUIEREN VISITARLA

## Incidencia de diabetes mellitus tipo 1 en las Islas ...

Jan 01, 2000 · Objective. To prospectively evaluate the incidence of type 1 diabetes mellitus in patients up to 30 years of age in the Canary Islands during 1995 and 1996. Patients and methods. The population under study consisted of 744,444 inhabitants in the 0-29 year old group and 302,293 in the 0-14 year old group.Se evaluo prospectivamente la incidencia de diabetes mellitus tipo 1 en menores de 30 anos en las Islas Canarias durante 1995 y 1996.Poblacion de ries….
From: www.sciencedirect.com

To study the incidence of type 1 diabetes (T1D) in children <14 years in the island of Gran Canaria (Canary Islands, Spain) during the 2006–2018 period and to evaluate its temporal trend, seasonality, age and sex distribution.

We studied children <14 years of age living in Gran Canaria. We calculated the annual and overall incidence using recorded data from the Pediatric Endocrinology Department as the primary source and the local Diabetes Association and the hospital's pharmacy as secondary sources. The primary source is the only paediatric endocrine unit in the island.

453 new T1D cases were observed during the 13-year period. The overall incidence of T1D between 2006 and 2018 was 30.48/100,000 (95% CI: 27.74–33.42). Distribution among age groups was 24.8%, 38.2% and 36.9% for children between 0–4, 5–9 and 10–13.9 years old respectively. No significant temporal trend, seasonality or sex differences were found.

Our study shows that the Island of Gran Canaria has one of the highest childhood incidences of T1D reported worldwide: among the highest rates in Europe, and higher than the rates published for the neighbouring African countries.

Estudiar la incidencia de diabetes mellitus tipo 1 (DM1) en ninos menores de 14 anos en la isla de Gran Canaria (Islas Canarias, Espana) durante el periodo 2006-2018, asi como evaluar su tendencia temporal, estacionalidad y distribucion por sexo y edad.

Los sujetos objeto de estudio fueron los ninos menores de 14 anos que habitan la isla de Gran Canaria. Calculamos la incidencia para todo el periodo, y la incidencia anual usando los datos recogidos en nuestra unidad de endocrinologia pediatrica como fuente primaria y los datos de la asociacion local de diabetes y la farmacia hospitalaria como fuentes secundarias. La fuente primaria es la unica unidad de endocrinologia pediatrica de la isla.

Observamos un total de 453 nuevos casos de DM1 durante el periodo de estudio. La incidencia global para el periodo 2006-2018 fue de 30,48/100.000 (IC 95%: 27,74-33,42). La distribucion por grupos de edad fue del 24,8, 38,2 y 36,9% para ninos entre 0-4, 5-9 y 10-13,9 anos de edad, respectivamente. No encontramos la aparicion de ninguna tendencia temporal significativa. Tampoco encontramos la presencia de estacionalidad ni diferencias significativas en cuanto a la aparicion de DM1 en base al sexo.

Nuestro estudio muestra que la isla de Gran Canaria presenta una de las incidencias de DM1 mas altas del mundo. Se encuentra entre las mas altas de Europa, y es claramente superior a la publicada para los paises vecinos africanos.

## Taller de Murga para niÃ±os LOS COLORINES

May 24, 2010 · TALLER DE MURGA PARA NIÃ‘OS “LOS COLORINES” Generalidades.
From: carnavaldeluruguay.com

TALLER DE MURGA PARA NIA‘OS «LOS COLORINES»

A partir del mes de mayo en el ya mA­tico local de Tras Bambalinas, comenzaremos un taller de murga para niA±os, que llamamos Los Colorines. En el taller, vamos a cantar, pintarnos, disfrazarnos, bailar y divertirnos con uno de los ritmos mas lindos del mundo… La Murga.

Los Colorines, tienen la direcciA³n de Fernando de Moraes, un murguero con muchos aA±os en carnaval y que ha dado talleres en Uruguay, Argentina, Francia y Estados Unidos, trabajando con murgueros niA±os, jA³venes y grandes. Empezamos a las 6 de la tarde y terminamos a las 7 y media, todos los jueves hasta fin de aA±o, donde haremos un gran tablado para mostrar todo lo que aprendimos en el aA±o. A este invierno…lo pintamos de murga con Los Colorines…!

Muchas gracias por la difusiA³n, y a las ordenes;
Fernando de Moraes

Taller Los Colorines
Informes e inscripciones: 096 798 690 095 596 615
[email protected]
DA­as y horarios: Jueves de 18 a 19 30 horas
Costo del taller: 350 pesos mensuales

## presa canario temperamento - debatesrogermarin.com

Oct 19, 2020 · El Presa Canario es un perro de tamaño medio/grande, fuerte, musculado y bien proporcionado, sin embargo pese a su imponente aspecto tiene un carácter dócil y un temperamento equilibrado. El hocico no es demasiado largo, pero si grande y fuerte, también de forma cuadrada que indica la potencia de su dentadura. Su perspicacia con ....
From: debatesrogermarin.com

Ambos son perros de presa de gran potencia, pero fisicamente difieren en cuanto a su morfologia y estetica.

And it allows the dog, with the mentality of a 3 year old at best, to make decisions on its own about who needs to be dealt with. Tambien comenzo a exportarse a otros lugares del mundo, aunque no es una de las razas mas numerosas en todo el globo. Su cofre es ancho y, ademA¡s, extremadamente profundo, y la espalda se cierra en cA­rculo en una cola gruesa que se aprieta hacia el final, de longitud media.

Desde los primeros majoreros, hasta el bulldog y el bulterrier que trajeron los ingleses hubo modificaciones y cambios genA©ticos a partir de la permisibilidad del cruce genA©tico de especies. temperamento es muy firme y ladra con gravedad. Los primeros perros de presa de las Islas Canarias eran majoreros, y datan del siglo XIV. Tuvieron que pasar diez anos hasta que comenzo la recuperacion de una variedad practicamente extinta. Todo propietarios de Presa Canario tiene que ser responsable y dedicarle el tiempo necesario a su perro desde cachorro. El perro de presa canario es un animal de tamano medio o grande, muy fuerte y robusto, con una notable masa muscular que sin embargo no le resta agilidad o potencia a la hora de moverse. Epilepsia (trastorno neurologico que causa convulsiones). Entre las caracteristicas del presa canario destaca su cabeza, de forma cuadrada y compacta, bastante grande en proporcion al resto de su cuerpo.

Se cree que esta raza surge en las Islas Canarias entre los siglos XV y XVI, tras la llegada de viajeros espanoles procedentes de los Pirineos que trajeron consigo algunas variedades autoctonas de la Peninsula como el Alano o el desaparecido Perro de presa Majorero.

## Abstract - OS

Sediments of the Adélie Basin were collected during the Integrated Ocean Drilling Program (IODP) Expedition from hole U1357B 318 in 2010. U1357B is located on the continental shelf off Wilkes Land at the Mertz Glacier polynya (region of open water surrounded by sea ice), Antarctica (66 ∘ 24.7990 " S, 140 ∘ 25.5705 " E), at about 1021.5 m water depth (Escutia et al., 2011) ….
From: os.copernicus.org

Due to its toxic nature and its high potential for biomagnification, mercury is a pollutant of concern. Understanding the marine biogeochemical cycle of mercury is crucial as consumption of mercury-enriched marine fish is the most important pathway of human exposure to monomethylmercury, a neurotoxin. However, due to the lack of long-term marine records, the role of the oceans in the global mercury cycle is poorly understood. We do not have well-documented data of natural mercury accumulations during changing environmental conditions, e.g., sea surface conditions in the ocean. To understand the influence of different sea surface conditions (climate-induced changes in ice coverage and biological production) on natural mercury accumulation, we used a continuous ∼170 m Holocene biogenic sedimentary record from Adelie Basin, East Antarctica, which mainly consists of silica-based skeletons of diatoms. We performed principal component analysis and regression analysis on element concentrations and corresponding residuals, respectively, to investigate the link between sediment mercury accumulation, terrestrial inputs, and phytoplankton productivity. Preindustrial mercury in the remote marine basin shows extremely high accumulation rates (median: 556 µg m-2 yr-1) that displayed periodic-like variations. Our analyses show that the variations in total mercury concentrations and accumulation rates are associated with biological production and related scavenging of water-phase mercury by rapidly sinking algae or algae-derived organic matter after intense algae blooms. High accumulation rates of other major and trace elements further reveal that, in regions of high primary productivity, settling of biogenic materials removes a large fraction of dissolved or particulate-bound elements from the free water phase through scavenging or biological uptake. The link between mercury cycling and primary production will need to be considered in future studies of the marine mercury cycle under primary production enhancement through climatic, temperature, and nutrient availability changes.

Mercury (Hg) is a metal of environmental concern due to its ability to be transported through the atmosphere from industrial point sources to remote regions and its transformations into highly bioaccumulative and neurotoxic methylated forms. In the global biogeochemical cycle of Hg, the ocean, as the dominant physical feature of our planet Earth, is of specific concern. A substantial amount of Hg ($∼80$ %) which is emitted to the atmosphere from natural and anthropogenic sources reaches the ocean (Horowitz et al., 2017; Schartup et al., 2019), and ocean sediments are considered to be the ultimate sink of Hg on a timescale of tens of thousands of years (Fitzgerald et al., 2007; Selin, 2009; Amos et al., 2013). Despite the important role of marine sedimentation in the global Hg biogeochemical cycle, little is known about the rates or amount of Hg accumulation in marine sediments, especially in the open ocean. In contrast to the well-studied Hg cycling in terrestrial environments, knowledge about the temporal and spatial distribution of Hg in the marine environment is limited to model estimations (Mason and Sheu, 2002; Sunderland and Mason, 2007), water column measurements (Cossa et al., 2011; Lamborg et al., 2014b; Canario et al., 2017), and very few sediment measurements (Kita et al., 2013; Aksentov and Sattarova, 2020). A main reason for our limited understanding of the fate of Hg in the oceans is the lack of high-resolution marine sedimentary records, especially from the deep ocean (Zaferani et al., 2018).

Hg input to the ocean is primarily through atmospheric deposition (Mason et al., 1994; Driscoll et al., 2013). After deposition, as either mercuric ion ($Hg2+$) or elemental Hg ($Hg0$), Hg can be reduced to $Hg0$ and evaded to the atmosphere or scavenged from the water column by particulate matter and eventually buried in deep-sea sediments (Mason et al., 2012; Lamborg et al., 2014a). Most marine surface waters are believed to be close to equilibrium between Hg deposition and evasion or saturated in $Hg0$ due to biologically mediated (Mason et al., 1995; Rolfhus and Fitzgerald, 2004; Whalin et al., 2007) and photochemical reduction (Amyot et al., 1997; Mason et al., 2001) followed by re-emission of $Hg0$ to the atmosphere. It has been estimated that almost 96 % of the deposited Hg to the ocean is lost through evasion from the surface, and only 30 % of the Hg flux that reaches the deep ocean is preserved in sediments (Mason and Sheu, 2002). However, other studies indicate that the ocean surface waters become a sink for atmospheric Hg at the high nutrient levels and related high primary productivity (Soerensen et al., 2016).

There are contradictory statements about the Hg deposition and evasion to/from different surface waters. Early works suggest that Hg evasion is high in productive upwelling regions of the ocean due to enhanced biological reduction (Fitzgerald et al., 1984; Mason and Fitzgerald, 1993). More recent studies, with higher spatial and temporal resolutions, suggest lower Hg evasion in productive regions (O'Driscoll et al., 2006; Qureshi et al., 2010; Soerensen et al., 2013, 2014). Measurements of Hg from these recent studies show relatively low concentrations of atmospheric and surface water-phase $Hg0$ in regions with high productivity compared to areas with lower productivity. These studies related their observation to sorption and scavenging of Hg by suspended organic particles. They suggested that removal of $Hg2+$ associated with sorption and scavenging by suspended organic particles in productive regions reduces the amount of available $Hg2+$ for reduction and re-emission. Therefore applying the model estimates across the entire ocean introduces substantial uncertainty, and one area in particular that highlights this uncertainty is the underestimation of the role of biological productivity as a major vector of Hg sedimentation in the oceans.

The marine biogeochemical cycle, especially sedimentation of many elements (Fowler and Knauer, 1986; Morel and Price, 2003; Schlesinger and Bernhardt, 2013), including Hg (Kita et al., 2013; Lamborg et al., 2016; Zaferani et al., 2018), in the ocean is controlled directly and indirectly by biological productivity. Biogenic particles control the distribution of elements through primary production, sinking, and decomposition (Fowler and Knauer, 1986). Besides direct uptake across cell membranes through active metabolism, phytoplankton and sinking biogenic particles can scavenge and remove many other elements from the dissolved phase and transport them to the deep sea. Sinking speed of biogenic particles plays an important role in the final fate of those elements. Rapidly sinking particles such as diatom agglomerates transfer elements to the deep sea (Fowler and Knauer, 1986; Smetacek et al., 2012), whereas elements associated with the slowly sinking particulates will release back to the water phase through remineralization (Fowler and Knauer, 1986). In areas where pronounced seasonal blooms take place, phytoplankton species appear to reach the deep-sea floor relatively fast and intact. Seasonal blooms in the surface waters will also cause temporally variable fluxes of elements in the deep ocean (Fowler and Knauer, 1986; Michel et al., 2002; Pilskaln et al., 2004). For Hg, these findings are supported by water column (Lamborg et al., 2014b) and marine sediment measurements (Kita et al., 2013; Aksentov and Sattarova, 2020). Lamborg et al. (2014b) described a nutrient-like distribution of Hg in the water column of oceans. This study indicates that, similar to carbon (C) and phosphorus (P), Hg shows higher concentrations in the deep water due to its release during organic matter decomposition. Kita et al. (2013) found a positive correlation between Hg and the absolute abundance of phytoplankton species in sediments of the Caribbean Sea. Hg in these sediments was assumed to be a result of Hg binding by phytoplankton depositing Hg-bearing organic matter in the photic zone. A similar conclusion was reached by Aksentov and Sattarova (2020), who used a thermoscanning technique to detect Hg forms. They found that biological productivity controlled the Hg burial in northwestern Pacific bottom sediments and that the forms of Hg depended on the diatom content.

These observations can be due to Hg–phytoplankton interactions and uptake or binding of Hg from the water by phytoplankton (Le Faucheur et al., 2014; Mason et al., 1996). This interaction controls the flux of Hg from the water column to sediments and facilitates the downward flux of Hg to the seafloor (Soerensen et al., 2014, 2016; Lamborg et al., 2016; Zaferani et al., 2018), which, as mentioned, has traditionally been considered to be slow in its nature. Thus, underestimating the role of biological productivity in the marine biogeochemical cycle of Hg may lead to an overestimation of re-emission fluxes from surface water and an underestimation of the Hg flux to deep-sea sediments.

In this context, the Southern Ocean is of particular interest due to its high concentrations of nutrients and related elevated primary productivity (Arrigo et al., 1998). In the Southern Ocean, diatoms are major primary producers (Crosta et al., 2005). Their siliceous cell walls preserve well in sediments and form diatom ooze (Futterer, 2006). The sedimentation rate of diatom ooze is high, estimated to reach up to 2 cm yr$-1$ (Escutia et al., 2011), making diatom ooze deposits around Antarctica a unique geochemical archive to study the influence of primary productivity as well as natural and anthropogenic changes on the marine biogeochemical cycle of Hg.

Despite providing a unique geochemical archive, studies on Hg cycling in the Southern Ocean, particularly in the Antarctic region, are generally limited to water column (Cossa et al., 2011; Nerentorp Mastromonaco et al., 2017b; Canario et al., 2017), surface water/air (Nerentorp Mastromonaco et al., 2017a; Wang et al., 2017), and ice core analyses (Vandal et al., 1993). Cossa et al. (2011) showed a nutrient-like distribution of Hg in the water column that ranged between 0.6 and 2.8 pmol L$-1$. Nerentorp Mastromonaco et al. (2017b) found higher total Hg concentration than Cossa et al. (2011), with no significant vertical variations. Both studies reported seasonal variations in Hg concentrations and related them to seasonal variations of atmospheric Hg deposition (Cossa et al., 2011; Nerentorp Mastromonaco et al., 2017b) as well as the Hg inputs from melting sea ice and snow (Nerentorp Mastromonaco et al., 2017b). Total Hg concentrations in the Atlantic sector of the Southern Ocean obtained during a study by Canario et al. (2017) were also, in general, comparable to those obtained by Cossa et al. (2011) except for some stations that showed higher total Hg concentrations. Canario et al. (2017) attributed these differences to the different stages of phytoplankton bloom during the sampling. This led to lower dissolved Hg in water in the middle–end stage of the bloom compared to the beginning stage of the bloom, owing to the Hg uptake by phytoplankton (Canario et al., 2017). Measurements of gaseous elemental mercury (GEM) and dissolved gaseous mercury (DGM) in surface water showed spatial and seasonal variations as well (Nerentorp Mastromonaco et al., 2017a; Wang et al., 2017). These studies related the increase in DGM and GEM concentrations to the presence and absence of sea ice. Sea ice that could prevent Hg evasion to the atmosphere could initially lead to an increase in Hg emissions to the atmosphere when diminishing. Hg concentrations in an ice core covering the past 34 kyr varied between 0.0005 and 0.0021 $µ$g kg$-1$, corresponding to depositional fluxes of 0.009 and 0.031 $µ$g m$-2$ yr$-1$ during the Holocene and the Last Glacial Maximum, respectively (Vandal et al., 1993). Vandal et al. (1993) attributed the observed enhanced Hg flux during colder periods to marine biological productivity and emissions of volatile Hg compounds from the ocean. The different results of the existing studies point to the gaps in our understanding of Hg behavior in productive remote areas, which warrants further investigation in the Southern Ocean.

In a previous paper, we discussed the accumulation of anthropogenic Hg in sediments of Adelie Basin, offshore East Antarctica. The $∼2$-fold increase in Hg concentrations and accumulation rates in the upper $∼2.80$ m depth of the core was attributed to the onset of the industrial revolution and the strong increase in coal burning at $∼1850$ CE (Zaferani et al., 2018). Here, we discuss the natural processes (e.g., changes in biogenic and terrestrial material fluxes) that controlled Hg accumulation in the same sediment core prior to 1850 CE throughout the past 8600 years. We investigated a continuous $∼170$ m long Holocene laminated diatom ooze sediment record from the Adelie Basin. Covering almost the entire Holocene, the core allows the determination of natural variations of Hg accumulation rates in these sediments prior to major anthropogenic influences. Our main objective was to investigate the influence of different Hg sources as well as climate-induced changes in biological productivity and terrestrial fluxes (through melting of glacier ice), which have controlled the sequestration of Hg in these sediments. To evaluate the influence of different biogeochemical processes on the Hg accumulation in sediments, with an emphasis on the role of changes in planktonic productivity, we combined the data on Hg accumulation with data derived from multielement analyses.

## A Novel Approach to Training Monotony and Acute-Chronic ...

May 31, 2021 · Twenty-seven professional soccer players (25.1 ± 2.9 years, 181.9 ± 6.3 cm, 73.1 ± 6.3 kg) were daily monitored over 25-week period using a microelectromechanical system [MEMS]. The inclusion criterion was that any given player could have skipped a maximum of one training session during that week and have participated in the full length of ...Load is a multifactorial construct, but usually reduced to parameters of volume and intensity. In the last decades, other constructs have been proposed for assessing load, but also relying on relationships between volume and intensity. For example, Foster's ....
From: www.ncbi.nlm.nih.gov

The aim of this work is therefore to propose a broader conceptual approach and new indices for assessing training monotony and acute to chronic workload. Specifically, there is an explicit attempt to integrate load orientation and weekly density (frequency normalized) into a novel load management strategy, while keeping a balance between depth and easiness of implementation without huge demands of time or technology in the field. Proxies for volume and external/internal load are also contemplated. Two models will be presented: one for assessing Intraweek Training Monotony (ITM), another to assess Acute to Chronic Workload Indices (ACWI). The goal behind both models is to deliver a new tool for monitoring training loads in a more complete, multidimensional manner, and to assist coaches in adjusting the planning. We invite researchers to conduct independent validation research concerning our proposals. We do not aim to provide the ultimate metric or the Holy Grail of load monitoring, nor will we attempt to state that our indices are in any way promoters of a reduction in injury risk. That is for the future to decide.

In the vein of composite load parameters, the concept of Training Monotony has slowly but steadily making its appearance in research (Delecroix et al., 2019; Clemente et al., 2020). Training Monotony (e.g., weekly) is mainly calculated in one of two manners: dividing the mean daily duration of the training sessions by the standard deviation, with or without having previously multiplied the session duration by the session-rating of perceived exertion (Foster, 1998). Therefore, the concept of Training Monotony is not reflecting the diversity of training contents. Furthermore, if weekly training sessions have similar duration and perceived exertion levels, that week will be considered monotonic, even if the specific contents and training stimuli diverged widely. In fact, even when multiplied by perceived exertion, this index is dominated by session duration (Weaving et al., 2020). Expanding the premise of Training Monotony to a larger number of weeks or even months, it is natural to arrive to models of Acute to Chronic Workload Ratios (ACWR), created with the purported goal of gaining a better insight and control over injury risk (Gabbett et al., 2016). The ACWR is calculated dividing the acute load (current week) by the so-called chronic load (usually the rolling 4-week average, or exponentially). Most studies calculating the ACWR use the rating of perceived exertion and time of session/competition in order to register the training load values, but others use total distance and distance in high-speed running (Gabbett et al., 2016; Clemente et al., 2019). Based on this data, there have been suggestions that injury likelihood increases when this ratio is above 1.5 arbitrary units (A.U.) and/or when it is low, within 0.8 to 1.3 A.U. (Soligard et al., 2016; Malone et al., 2017). However, association should not be confused with causation (Stovitz et al., 2019). Recent research has questioned the validity of using the ACWR to predict injury risk (Fanchini et al., 2018; Enright et al., 2020; Impellizzeri et al., 2020a; Sedeaud et al., 2020; West et al., 2020) and called for a re-framing of the conceptual model behind the ACWR (Impellizzeri et al., 2020b; Kalkhoven et al., 2021).

Sports Sciences have, however, been overly focused in training variables such as volume and intensity (Bradbury et al., 2020), and sometimes frequency (Schoenfeld et al., 2019), while largely neglecting other dimensions of load (Piggott et al., 2019). Volume and intensity are important load parameters (Mangine et al., 2015), but they provide an incomplete picture. For example, training frequency seems to be an important load parameter, even when the training programs have equal volumes (Ochi et al., 2018). Research on exercise prescription has even explored the minimal effective doses for preserving endurance and strength over time (Spiering et al., 2021), but load was defined by these three parameters: intensity, volume and frequency. Although frequency has been equated with weekly density (Bompa and Buzzichelli, 2018), training density could reflect changes in rest periods, for the same workload (La Scala Teixeira et al., 2019), i.e., density can represent a measure of how compact the workload was, establishing a relationship between the number of training sessions relative to a 7-day period. In a sense, weekly density provides an assessment of frequency normalized to the week. Load complexity has also been proposed as an important composite parameter, loosely defined as reflecting the degree of sophistication or difficulty of a skill (Bompa and Buzzichelli, 2018), or as reflecting the difficulty, variability and uncertainty involved in the actions to be performed (La Scala Teixeira et al., 2019). Currently, there is no satisfactory operational definition of load complexity.

Training is a multifactorial process (Bompa and Buzzichelli, 2018) where a delicate balance between load and recovery should be achieved (Kellmann et al., 2018). Such balance will hopefully allow improvements in performance while diminishing overuse injuries and drop-out rates (Schwellnus et al., 2016; Soligard et al., 2016; Aicale et al., 2018), although these issues remain complex and controversial (Kalkhoven et al., 2021). Planning has a role to play in this context (Bompa and Buzzichelli, 2018), but monitoring the actual training load and the athletes' responses is paramount (Griffin et al., 2020; McGuigan et al., 2020). While coaches' memory may be biased and partial (Brawley, 1984; Laird and Waters, 2008), an accurate recording of what was actually performed provides a more objective basis for understanding the dynamics of the process and support future decision-making. But defining what training load is and actually measuring it is a very complex subject matter (Mujika, 2017; Afonso et al., 2018) and should not be reduced to a single magical number (Impellizzeri et al., 2020a). Indeed, several parameters can be used to define training load: volume, intensity, frequency, density, monotony, orientation, complexity, among others (ACSM, 2018; Bompa and Buzzichelli, 2018; Delecroix et al., 2019).

Metrics of external and/or internal loads: (i) Although the models only accept one value for the metrics parameter, this value can originate from a single metric, or a from a combination of metrics, and is considered to reflect the load of the training session as a whole. Each coach should decide what the relevant and/or readily accessible metrics are, and how to combine them. The model will treat the value introduced by the coach but will not limit the origin of that value. This provides plasticity and means that the model can be applied to different sports and realities. (ii) ITM analyses inter-session variation of these metrics, while ACWI analyses inter-week variations. In our model, only total distance was used, to provide a simple example for the coaches.

While ITM reflects independent values for each training week, ACWI provides interdependent values, as the current week and past weeks will have an impact on the index. Therefore, the index is dynamic and will evolve as more information is brought. However, weeks closer to the acute week will have a greater impact than weeks farther from the acute week. Both ITM and ACWI consider four parameters: (i) session duration as a proxy for session volume; (ii) weekly density (i.e., frequency normalized); (iii) metrics of external and/or internal load; and (iv) intersession or interweek repeatability, which is deeply related to load orientation. Our original idea was to attribute differential weights to the different components of the models. However, we could not find solid support on the literature and so in the current version all the factors have the same weight in the equations. Beyond the similarities, there are specificities to each model:

Two conceptual models were developed: an Intraweek Training Monotony Index (ITM) and an Acute:Chronic Workload Index (ACWI). The main goal was to provide a tool to assess load dynamics that incorporates load orientation and weekly density (frequency normalized) in addition to proxies of volume and external/internal load. presents the proposed hierarchical model for categorizing load orientation. Each training session may have one or more load orientations, depending on how the coaches organize the session. For example, the coach may start with mobility work, then proceed to speed work, followed by technically driven skills and small-sided games, and therefore this particular training session would have four different load orientations.

The commonly used formula for calculating Training Monotony was originally proposed by Foster (1998). In the original proposal, session duration was multiplied by the session-RPE (sRPE), and this product was termed the “session load.” If multiple daily sessions were performed, a simple sum was performed, and a single daily value was obtained. Each week, the mean and standard deviation (SD) for this session load were calculated, and the division of the mean by the SD (i.e., the inverse of a typical coefficient of variation) provided the value for “monotony.” Our data does not contain sRPE, but as was established in the introduction, even when multiplied by sRPE, Foster's index is still dominated by session duration.

A MEMS (JOHAN Sports, Noordwijk, The Netherlands) consisting in 10-Hz Global Positioning System (GPS) including EGNOS correction and an accelerometer, gyroscope, and magnetometer (100 Hz, 3 axes, ±16 g) were used. A previous study reported validity and reliability results of this device for monitoring external load (Nikolaidis et al., 2018). The MEMS unit was always used by the same player to reduce inter-unit variability error. The unit was placed in a custom-design bag within a vest. The unit was fixed between the scapulae of the players. The data was recorded throughout each training session, namely including the moments of warm-up, breaks and cool-down. The same observer recorded all the periods of exercises and, after that, has split the data based on those periods. The variables that was collected for each session and that was used as a proxy of internal load was total distance (TD: consisting in total number of meters covered by a player during the session/exercise). Here, as our goal was merely to present a proof of concept with a simple interpretation, and so only total distance was used, as it is an easy metric to collect and interpret.

Twenty-seven professional soccer players (25.1 ± 2.9 years, 181.9 ± 6.3 cm, 73.1 ± 6.3 kg) were daily monitored over 25-week period using a microelectromechanical system [MEMS]. The inclusion criterion was that any given player could have skipped a maximum of one training session during that week and have participated in the full length of the remaining sessions. Participants were informed about the study design and methodology. The procedures were part of their daily sport activities. All of them signed a free consent about their inclusion in the study. The study followed the ethical standards of Declaration of Helsinki for the study in humans.

In , it is interesting to analyse the two points that were highlighted through a dashed circumference, since they demonstrate how different ACWR and ACWI can be. These two points have an ACWR close to 1, but their ACWI is <0.4. Both these points occurred in week 13, corresponding to two players. One of the players only performed one training session in this week and limited to two training orientations, while in the previous weeks he had an average of three weekly training sessions and performed five training orientations. By assessing load orientation and also weekly density, ACWI was able to capture this very abrupt changes better than ACWR, despite total distance having been similar across these weeks (the player performed only one session, but with very high total distance). For the second player, a similar phenomenon occurred, although with total distance being considerably lower in week 13.

In both ACWR and ACWI, care should be taken to avoid misinterpreting week to week differences, as in the two models there is influence from previous weeks. For example: in the abrupt changes seen from weeks 23 to 24, ACWI is being influenced by the 22 and 23 previous weeks, respectively. So, analysis of pairs of weeks is not advised. Also, when comparing ACWR and ACWI, there was no meaningful correlation between the models (r2 = 0.0057) (). Although the ACWI values are concentrated between 0 and 2 A.U., they could theoretically reach 3 A.U. The lack of correlation demonstrates that the models are conveying qualitatively different information.

As previously established, the novel ACWI was contrasted to the former uncoupled ACWR with EWMA. In , data comparing the ACWR and ACWI is presented. ACWR requires a minimum of 4 chronic weeks, and therefore data can only be calculated starting on week 5. Contrariwise, ACWI accepts any number of chronic weeks, and the week n always presents the greatest weight, followed by n-1, n-2, and so forth. The smaller the weight of any given week in the model, the less the index fluctuates in light of that week's value. For example, if four chronic weeks are considered, the weight of the chronic week, when 4 weeks are considered, is ~40% of total. Starting in the 8th week, the relative weight of the first week in ACWI is reduced to ~22%. The ACWI can therefore be used continuously throughout the season, with no upper limit of weeks that can be used to calculate it.

The aforementioned differences can also be visualized in and their numerical values presented in . In Foster's Training Monotony, the data points organize more strongly into clusters, which is expected due its strong reliance of session duration, that will tend to be similar for different players. Although our data does not have sRPE, it was previously established that in Foster's model session duration outweighs sRPE. Furthermore, since sRPE has discrete values, multiplying session duration by sRPE would only have subdivided the clusters and grouping the players with equal sRPE. On the other hand, ITM presents a greater individualization (and scattering) of the obtained values. As dissected in , the coefficients of variation (CV) of ITM for each cluster of points are superior to the CV of Foster's Training Monotony. So, in each cluster, ITM demonstrates greater variation from player to player than Foster's Index, suggesting that it provides a more highly individualized set of values. In fact, the ratio of ITM CV to Foster's CV varied from 3.000 to 9.769, i.e., ITM's CV was 3 to ~9.8 times superior to that of Foster's Index for each cluster. In cluster 7, all Foster's values were equal, and consequently CV was zero. As such, the ratio of CVs could not be calculated for cluster 7. Therefore, while the average-based analysis suggests that Foster's Training Monotony and ITM convey similar information, individualized analysis show a very different picture. And in this first, simplified application of our model, only total distance was used as a proxy for load, but the model allows introducing combinations of metrics, thereby extending individualization even further.

In , the data for ITM and Foster's Index is presented for two selected players. The left-side graph depicts the flow of ITM and Foster's Index for Player “A.” The right-side graph depicts the flow of ITM and Foster's Index for Player “B.” The two players were chosen purposefully according to the following criteria: (i) having data points available for all 25 weeks; and (ii) providing qualitatively distinct dynamics of ITM in relation to Foster's Index. While for Player “A,” ITM seemed virtually identical to Foster's Index in terms its qualitative behavior, for Player “B” the behavior of ITM deviated more prominently. For both players, in weeks 12 to 14 ITM deviated considerably from Foster's Index, a trend that had already been analyzed globally. For each player, the residual sum of squares (RSS) was calculated: the smaller this sum, the greater the similarity between the two models. For player “A,” RSS was 0.286. Conversely, for player “B” RSS was 0.555, i.e., for player “B” there was a greater difference between Foster's Monotony Index and ITM. It is interesting to also note week 5: while player “A” exhibited a decreased in ITM, player “B” sharply increased ITM. The raw data showed that players “A” and “B” were exposed to similar load orientations, but player “A” had an exposition to analytical technical drills, a type of load orientation that was absent for player “B” in that particular. This introduced greater heterogeneity for player “A,” contributing for a reduction in monotony. Moreover, in that same week, player “A” had intersession variations in training distance of up to 43%, while in player “B” those intersession variations were limited to a maximum of 22%. These two factors concurred for player “B” to experience a sharp increase in ITM.

presents an overall comparison of the 27 athletes between Foster's Training Monotony and ITM, while , present the individual data for both models. The overall view of the models suggest they provide similar information (r2 = 0.85), with a few notable exceptions in weeks 12 and 14. Analyzing the raw data, it can be seen that from week 12 to week 14, there was a 32.7% average increase in total distance. There were also notable changes in load orientation. For example, in week 12, simulated full competition comprised 7.98% of the loads, while in week 14 in represented 20.06%. Conversely, analytical technical drills represented 16.81% of loads in week 12, but only 4.79% in week 14. Also of note, tactically driven drills represented 8.40% of loads in week 12, and 24.55% in week 14. In these weeks, ITM and Foster's Index behaved differently.

Discussion

First and foremost, it should be noted that ACWI is not merely an extension of ITM to more than 1 week. For example, if a coach uses an intraweek training structure that is highly diversified, ITM will be low. But, if the coach repeats that intraweek structure week after week, ACWI will be high. In this particular scenario, AWCR would be close to 1, as acute:chronic workloads would be considered to be stable, but in our ACWI model the values would be high. Conversely, 10 weeks with very high ITMs may compound a low ACWI, as long as those 10 weeks are sufficiently different from each other. Evidently, depending on the planning, the coach may wish to strategically design monotonous training weeks, or even monotonous training periods in some phases of the season (Bompa and Buzzichelli, 2018). Therefore, monotony is not an inherently negative concept. Moreover, it should be highlight that our ACWI can be calculated with as little as one acute week and one chronic week and has no upper limit to the number of chronic weeks than can be included, although the further back in time, the smaller the weight of that specific week.

Our results have shown that the proposed models (ITM and ACWI) provide information that is distinct from previous models for assessing Training Monotony and ACWR, both quantitatively and, most notably, qualitatively. In the case of ITM, the average data is similar to that provided by Foster's Training Monotony, but the individualized analysis provided a very different picture. Possibly, with future expansions of the model and incorporation of more and more diversified proxies of internal and/or external load, ITM will become even more distinct from Foster's ITM. As for ACWR and ACWI, even the average data shows that we are facing two qualitatively very distinct models, and the incorporation of load orientation is a particularity that clearly distinguishes the two models. This, in itself, is relevant, because it confirms that the models are not redundant, instead they provide distinct metrics. For example, ITM seems to provide information that is more sensitive to each player's profile, since it is not overly reliant on session duration and it is able to deal with a more complex set of metrics that better reflect internal and/or external load. From the perspective of the coach, we are bringing to the table a different instrument for monitoring load, where load orientation plays a prominent role and there is greater flexibility in terms of which metrics of load (i.e., proxies of internal and/or external load) can be used. Together, ITM and ACWI can be used to provide a novel understanding of load dynamics and assist coaches in monitoring their training process; while ITM should be applied to a training week, the ACWI represents a long-term model. Finally, it is important to highlight that ACWI can be used continuously throughout the season, i.e., there is no upper limit to the number of weeks that can be input, even if their weight diminishes as time passes. One limitation of our dataset is that no data was available for official matches, and so the calculations were performed using only data from training sessions. However, it is important emphasize that the model allows use both (training sessions and matches).

Our models are not without limitations. In future iterations of the model, sRPE should be integrated in the metrics (which we did not do, since our data were not originally collected with the purpose of testing these models). Furthermore, it might be questioned if session duration is the best metric for assessing session training volume, or if alternative metrics such as intra-training density (Bompa and Buzzichelli, 2018), player training load (Bredt et al., 2020) or concepts exported from pedagogy such as learning time (Siedentop et al., 1982; Whipp et al., 2015) could better reflect the actual daily training volume. Again, future iterations of the model could explore these alternatives. Additionally, the sequencing or ordering of different load directions within the same training session may also be a relevant factor (Sanchez-Sanchez et al., 2018, 2019), but we have not included this factor to avoid excessive complexity. While the factor for metrics of internal and/or external is very open and accepts different inputs, it will most likely be associated with assessments such as percentage of repetition maximum, distance covered in sprint, and other physically dominated parameters. However, this parameter of our model may as well incorporate the cognitive, decisional and emotional impact of load imposed on the athletes (Collins et al., 2018; Avila-Gandia et al., 2020) and other related concepts. This makes the model customizable and adaptable to the coaches' training philosophy and can allow individual solutions to the athlete preparation puzzle. This is undoubtedly the advantage of the current model in comparison with the previous models, based on pre-defined proxies of load. Additionally, the parameters of the model may evolve in line with the evolution of scientific knowledge, practical experience and technological innovations.

Also, as was previously recognized, our data was not collected with the specific purpose of testing ITM and ACWI, which limits the full testing of these models. However, due to the Covid-19 pandemic, we were unable to collect further data. Still, we felt it would be relevant to expose these new theoretical models to a wider audience, as we are strongly convicted that these ideas may prove useful for Sports Sciences, even if future iterations do not use these specific models. Most importantly, we believe that these models should be used on an individualized basis, avoiding the stipulation of average, arbitrary cut-off values. We know that future research will likely link ITM and ACWI to overall performance and/or injury risk, but again we would advise against the simplistic attempts to find the “magic number.” Beyond inter- and intraindividual variability in response to training, planning will also likely interfere with ITM and ACWI, as different phases of the season tend to have different demands, and these can be by design (Bompa and Buzzichelli, 2018).

We do believe that the main scientific contribution of the new proposed workload measures is related to the capacity of clearly defined variability based on the dimensions of load and structural concept of the drills which was not considered in any other workload measure, as far we know. Additionally, the newly proposed ACWI also provides a good reference for identifying the progression of load, with a strong capacity of integrating any kind of information and without the limitation of getting the previous history of load which is very relevant for particular cases as pre-season or return-to-play after a period of training absence.

## OS - Biogeochemical processes accounting for the natural ...

Abstract. Due to its toxic nature and its high potential for biomagnification, mercury is a pollutant of concern. Understanding the marine biogeochemical cycle of mercury is crucial as consumption of mercury-enriched marine fish is the most important pathway of human exposure to monomethylmercury, a neurotoxin.

Abstract. Due to its toxic nature and its high potential for biomagnification, mercury is a pollutant of concern. Understanding the marine biogeochemical cycle of mercury is crucial as consumption of mercury-enriched marine fish is the most important pathway of human exposure to monomethylmercury, a neurotoxin. However, due to the lack of long-term marine records, the role of the oceans in the global mercury cycle is poorly understood. We do not have well-documented data of natural mercury accumulations during changing environmental conditions, e.g., sea surface conditions in the ocean. To understand the influence of different sea surface conditions (climate-induced changes in ice coverage and biological production) on natural mercury accumulation, we used a continuous ∼170 m Holocene biogenic sedimentary record from Adelie Basin, East Antarctica, which mainly consists of silica-based skeletons of diatoms. We performed principal component analysis and regression analysis on element concentrations and corresponding residuals, respectively, to investigate the link between sediment mercury accumulation, terrestrial inputs, and phytoplankton productivity. Preindustrial mercury in the remote marine basin shows extremely high accumulation rates (median: 556 µg m−2 yr−1) that displayed periodic-like variations. Our analyses show that the variations in total mercury concentrations and accumulation rates are associated with biological production and related scavenging of water-phase mercury by rapidly sinking algae or algae-derived organic matter after intense algae blooms. High accumulation rates of other major and trace elements further reveal that, in regions of high primary productivity, settling of biogenic materials removes a large fraction of dissolved or particulate-bound elements from the free water phase through scavenging or biological uptake. The link between mercury cycling and primary production will need to be considered in future studies of the marine mercury cycle under primary production enhancement through climatic, temperature, and nutrient availability changes.

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From: os.copernicus.org

Mercury (Hg) is a metal of environmental concern due to its ability to be transported through the atmosphere from industrial point sources to remote regions and its transformations into highly bioaccumulative and neurotoxic methylated forms. In the global biogeochemical cycle of Hg, the ocean, as the dominant physical feature of our planet Earth, is of specific concern. A substantial amount of Hg (∼80 %) which is emitted to the atmosphere from natural and anthropogenic sources reaches the ocean (Horowitz et al., 2017; Schartup et al., 2019), and ocean sediments are considered to be the ultimate sink of Hg on a timescale of tens of thousands of years (Fitzgerald et al., 2007; Selin, 2009; Amos et al., 2013). Despite the important role of marine sedimentation in the global Hg biogeochemical cycle, little is known about the rates or amount of Hg accumulation in marine sediments, especially in the open ocean. In contrast to the well-studied Hg cycling in terrestrial environments, knowledge about the temporal and spatial distribution of Hg in the marine environment is limited to model estimations (Mason and Sheu, 2002; Sunderland and Mason, 2007), water column measurements (Cossa et al., 2011; Lamborg et al., 2014b; Canario et al., 2017), and very few sediment measurements (Kita et al., 2013; Aksentov and Sattarova, 2020). A main reason for our limited understanding of the fate of Hg in the oceans is the lack of high-resolution marine sedimentary records, especially from the deep ocean (Zaferani et al., 2018).

Hg input to the ocean is primarily through atmospheric deposition (Mason et al., 1994; Driscoll et al., 2013). After deposition, as either mercuric ion (Hg2+) or elemental Hg (Hg0), Hg can be reduced to Hg0 and evaded to the atmosphere or scavenged from the water column by particulate matter and eventually buried in deep-sea sediments (Mason et al., 2012; Lamborg et al., 2014a). Most marine surface waters are believed to be close to equilibrium between Hg deposition and evasion or saturated in Hg0 due to biologically mediated (Mason et al., 1995; Rolfhus and Fitzgerald, 2004; Whalin et al., 2007) and photochemical reduction (Amyot et al., 1997; Mason et al., 2001) followed by re-emission of Hg0 to the atmosphere. It has been estimated that almost 96 % of the deposited Hg to the ocean is lost through evasion from the surface, and only 30 % of the Hg flux that reaches the deep ocean is preserved in sediments (Mason and Sheu, 2002). However, other studies indicate that the ocean surface waters become a sink for atmospheric Hg at the high nutrient levels and related high primary productivity (Soerensen et al., 2016).

There are contradictory statements about the Hg deposition and evasion to/from different surface waters. Early works suggest that Hg evasion is high in productive upwelling regions of the ocean due to enhanced biological reduction (Fitzgerald et al., 1984; Mason and Fitzgerald, 1993). More recent studies, with higher spatial and temporal resolutions, suggest lower Hg evasion in productive regions (O'Driscoll et al., 2006; Qureshi et al., 2010; Soerensen et al., 2013, 2014). Measurements of Hg from these recent studies show relatively low concentrations of atmospheric and surface water-phase Hg0 in regions with high productivity compared to areas with lower productivity. These studies related their observation to sorption and scavenging of Hg by suspended organic particles. They suggested that removal of Hg2+ associated with sorption and scavenging by suspended organic particles in productive regions reduces the amount of available Hg2+ for reduction and re-emission. Therefore applying the model estimates across the entire ocean introduces substantial uncertainty, and one area in particular that highlights this uncertainty is the underestimation of the role of biological productivity as a major vector of Hg sedimentation in the oceans.

The marine biogeochemical cycle, especially sedimentation of many elements (Fowler and Knauer, 1986; Morel and Price, 2003; Schlesinger and Bernhardt, 2013), including Hg (Kita et al., 2013; Lamborg et al., 2016; Zaferani et al., 2018), in the ocean is controlled directly and indirectly by biological productivity. Biogenic particles control the distribution of elements through primary production, sinking, and decomposition (Fowler and Knauer, 1986). Besides direct uptake across cell membranes through active metabolism, phytoplankton and sinking biogenic particles can scavenge and remove many other elements from the dissolved phase and transport them to the deep sea. Sinking speed of biogenic particles plays an important role in the final fate of those elements. Rapidly sinking particles such as diatom agglomerates transfer elements to the deep sea (Fowler and Knauer, 1986; Smetacek et al., 2012), whereas elements associated with the slowly sinking particulates will release back to the water phase through remineralization (Fowler and Knauer, 1986). In areas where pronounced seasonal blooms take place, phytoplankton species appear to reach the deep-sea floor relatively fast and intact. Seasonal blooms in the surface waters will also cause temporally variable fluxes of elements in the deep ocean (Fowler and Knauer, 1986; Michel et al., 2002; Pilskaln et al., 2004). For Hg, these findings are supported by water column (Lamborg et al., 2014b) and marine sediment measurements (Kita et al., 2013; Aksentov and Sattarova, 2020). Lamborg et al. (2014b) described a nutrient-like distribution of Hg in the water column of oceans. This study indicates that, similar to carbon (C) and phosphorus (P), Hg shows higher concentrations in the deep water due to its release during organic matter decomposition. Kita et al. (2013) found a positive correlation between Hg and the absolute abundance of phytoplankton species in sediments of the Caribbean Sea. Hg in these sediments was assumed to be a result of Hg binding by phytoplankton depositing Hg-bearing organic matter in the photic zone. A similar conclusion was reached by Aksentov and Sattarova (2020), who used a thermoscanning technique to detect Hg forms. They found that biological productivity controlled the Hg burial in northwestern Pacific bottom sediments and that the forms of Hg depended on the diatom content.

These observations can be due to Hg–phytoplankton interactions and uptake or binding of Hg from the water by phytoplankton (Le Faucheur et al., 2014; Mason et al., 1996). This interaction controls the flux of Hg from the water column to sediments and facilitates the downward flux of Hg to the seafloor (Soerensen et al., 2014, 2016; Lamborg et al., 2016; Zaferani et al., 2018), which, as mentioned, has traditionally been considered to be slow in its nature. Thus, underestimating the role of biological productivity in the marine biogeochemical cycle of Hg may lead to an overestimation of re-emission fluxes from surface water and an underestimation of the Hg flux to deep-sea sediments.

In this context, the Southern Ocean is of particular interest due to its high concentrations of nutrients and related elevated primary productivity (Arrigo et al., 1998). In the Southern Ocean, diatoms are major primary producers (Crosta et al., 2005). Their siliceous cell walls preserve well in sediments and form diatom ooze (Futterer, 2006). The sedimentation rate of diatom ooze is high, estimated to reach up to 2 cm yr−1 (Escutia et al., 2011), making diatom ooze deposits around Antarctica a unique geochemical archive to study the influence of primary productivity as well as natural and anthropogenic changes on the marine biogeochemical cycle of Hg.

Despite providing a unique geochemical archive, studies on Hg cycling in the Southern Ocean, particularly in the Antarctic region, are generally limited to water column (Cossa et al., 2011; Nerentorp Mastromonaco et al., 2017b; Canario et al., 2017), surface water/air (Nerentorp Mastromonaco et al., 2017a; Wang et al., 2017), and ice core analyses (Vandal et al., 1993). Cossa et al. (2011) showed a nutrient-like distribution of Hg in the water column that ranged between 0.6 and 2.8 pmol L−1. Nerentorp Mastromonaco et al. (2017b) found higher total Hg concentration than Cossa et al. (2011), with no significant vertical variations. Both studies reported seasonal variations in Hg concentrations and related them to seasonal variations of atmospheric Hg deposition (Cossa et al., 2011; Nerentorp Mastromonaco et al., 2017b) as well as the Hg inputs from melting sea ice and snow (Nerentorp Mastromonaco et al., 2017b). Total Hg concentrations in the Atlantic sector of the Southern Ocean obtained during a study by Canario et al. (2017) were also, in general, comparable to those obtained by Cossa et al. (2011) except for some stations that showed higher total Hg concentrations. Canario et al. (2017) attributed these differences to the different stages of phytoplankton bloom during the sampling. This led to lower dissolved Hg in water in the middle–end stage of the bloom compared to the beginning stage of the bloom, owing to the Hg uptake by phytoplankton (Canario et al., 2017). Measurements of gaseous elemental mercury (GEM) and dissolved gaseous mercury (DGM) in surface water showed spatial and seasonal variations as well (Nerentorp Mastromonaco et al., 2017a; Wang et al., 2017). These studies related the increase in DGM and GEM concentrations to the presence and absence of sea ice. Sea ice that could prevent Hg evasion to the atmosphere could initially lead to an increase in Hg emissions to the atmosphere when diminishing. Hg concentrations in an ice core covering the past 34 kyr varied between 0.0005 and 0.0021 µg kg−1, corresponding to depositional fluxes of 0.009 and 0.031 µg m−2 yr−1 during the Holocene and the Last Glacial Maximum, respectively (Vandal et al., 1993). Vandal et al. (1993) attributed the observed enhanced Hg flux during colder periods to marine biological productivity and emissions of volatile Hg compounds from the ocean. The different results of the existing studies point to the gaps in our understanding of Hg behavior in productive remote areas, which warrants further investigation in the Southern Ocean.

In a previous paper, we discussed the accumulation of anthropogenic Hg in sediments of Adelie Basin, offshore East Antarctica. The ∼2-fold increase in Hg concentrations and accumulation rates in the upper ∼2.80 m depth of the core was attributed to the onset of the industrial revolution and the strong increase in coal burning at ∼1850 CE (Zaferani et al., 2018). Here, we discuss the natural processes (e.g., changes in biogenic and terrestrial material fluxes) that controlled Hg accumulation in the same sediment core prior to 1850 CE throughout the past 8600 years. We investigated a continuous ∼170 m long Holocene laminated diatom ooze sediment record from the Adelie Basin. Covering almost the entire Holocene, the core allows the determination of natural variations of Hg accumulation rates in these sediments prior to major anthropogenic influences. Our main objective was to investigate the influence of different Hg sources as well as climate-induced changes in biological productivity and terrestrial fluxes (through melting of glacier ice), which have controlled the sequestration of Hg in these sediments. To evaluate the influence of different biogeochemical processes on the Hg accumulation in sediments, with an emphasis on the role of changes in planktonic productivity, we combined the data on Hg accumulation with data derived from multielement analyses.

## Are Heart Rate and Rating of Perceived Exertion Effective ...

Jul 04, 2020 · 1. Introduction. The practice of indoor cycling (IC) is a very common group activity in gyms and health clubs. One of the goals of the practice of IC is to improve some parameters, for example, body composition, a decrease in body mass, in fat free mass, an improvement in muscle mass, a decrease in body perimeter (ex. calf, thigh, abdominal, chest, and arm size), a …Indoor cycling’s popularity is related to the combination of music and exercise leading to higher levels of exercise intensity. It was our objective to determine the efficacy of heart rate and rating of perceived exertion in controlling the intensity ....
From: www.ncbi.nlm.nih.gov

Indoor cycling’s popularity is related to the combination of music and exercise leading to higher levels of exercise intensity. It was our objective to determine the efficacy of heart rate and rating of perceived exertion in controlling the intensity of indoor cycling classes and to quantify their association with oxygen uptake. Twelve experienced males performed three indoor cycling sessions of 45 min that differed in the way the intensity was controlled: (i) oxygen uptake; (ii) heart rate; and (iii) rating of perceived exertion using the OMNI-Cycling. The oxygen uptake levels were significantly higher (p = 0.007; μp2 = 0.254) in oxygen uptake than heart rate sessions. Oxygen uptake related to body mass was significantly higher (p < 0.005) in the oxygen uptake sessions compared with other sessions. Strong correlations were observed between oxygen uptake mean in the oxygen uptake and rating of perceived exertion sessions (r =0.986, p < 0.0001) and between oxygen uptake mean in the oxygen uptake and heart rate sessions (r = 0.977, p < 0.0001). Both heart rate and rating of perceived exertion are effective in controlling the intensity of indoor cycling classes in experienced subjects. However, the use of rating of perceived exertion is easier to use and does not require special instrumentation.

1. Introduction

The practice of indoor cycling (IC) is a very common group activity in gyms and health clubs. One of the goals of the practice of IC is to improve some parameters, for example, body composition, a decrease in body mass, in fat free mass, an improvement in muscle mass, a decrease in body perimeter (ex. calf, thigh, abdominal, chest, and arm size), a decrease in resting heart rate (HR), and also an improvement in oxygen uptake (VO2). In order to maximize the benefits and minimize the risks, controlling intensity in IC classes is very important. In these classes, intensity control can be carried out through heart rate (% of maximum or reserve), metabolic equivalents, oxygen uptake, power output, and rate of perceived exertion (RPE) [1].

HR is the variable most frequently recommended for controlling intensity in IC [2,3]. However, besides the variations of the intensity of the exercise [4,5], there are multiple factors that can bias the HR, thus the HR can increase or decrease without intensity being the direct cause. These factors include cycling rhythm [6,7], ambient temperature [8,9], state of hydration [10], and music stimuli [11]. In contrast, studies have shown that there is no impact on HR when exercise is carried out in environments with sound and visual stimuli [12] and with various states of hydration. [13] As for how HR responds to exercise intervals, there is a consensus that responses to intensity change are comparatively slow, contrary to continuous efforts with constant intensity [14].

Measures of metabolic rate, namely VO2, are precise indicators in establishing exercise intensity. However, the equipment required to measure VO2 is expensive and may be available only in a laboratory environment. Power output is one of the most reliable forms of controlling effort intensity in cycling [15]. In order for power output to be determined, there is a need to quantify the cycling cadence, and the resistance applied to the bike or the force applied to the pedal, as well as the flywheel circumference or, alternatively, to have a power meter fitted to the crank, crank arm, or pedal. This requires expensive equipment, which is normally not available in places where indoor cycling is practiced for recreational purposes and to improve health and performance. On the other hand, the use of RPE is an easy and inexpensive way to self-regulate intensity [16]. This method allows for anatomical differentiation of the exertion signal associated with the legs and chest as well as the global or whole-body perception of exertion [17]. The most frequently used RPE scales are the Borg (6–20), the Borg CR-10 (0–10), and the OMNI-Cycle scale (0–10) [18]. These RPE scales have been shown to be reliable in the control of exercise intensity during cycling for men and women [19,20] who are familiar with the effort scale and with the exercise mode [21]; however, some authors assert that exercise intensity control during IC is best carried out through the use of HR [3,4,5,22].

Therefore, due to these discrepancies in the literature, the aim of this study was to determine if the use of heart rate and rating of perceived exertion are effective means to control the intensity of indoor cycling classes and to quantify their association with oxygen uptake.

This study is pertinent because there is a lack of consensus in the existing literature concerning the safety of IC, as the intensity has been shown to exceed the levels of maximum effort reached in the laboratory [4,5]. Therefore, it is important to find a safe and effective way to self-regulate intensity during IC.

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