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8.11: Branch Migration - Biology

8.11: Branch Migration - Biology


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The movement of a Holliday junction to generate additional heteroduplex requires two proteins. One is the RuvA tetramer, which recognizes the structure of the Holliday junction. A rendering of the structure derived from X-ray crystallographic analysis of the RuvA-Holliday junction crystals is shown in Figure 8.18.

Figure 8.18: Three-dimensional structure of the RuvA tetramer complexed with a Holliday junction [from Hargreaves et al. (1998) Nature Structural Biology 5: 441-4460]. For the RuvA protein, alpha helices are green cylinders, beta sheets are brown arrows and loops are blue. The four strands of the two duplexes in the Holliday junction are red lines. The atomic coordinates were downloaded from the Molecular Structure database at NCBI, rendered in Cn3D v.3.0, and a pict file obtained as a screen shot. The kin file for viewing the virtual 3-D image on your own computer is accessible at the course web site.

RuvB is an ATPase. It forms hexameric rings that provide the motor for branch migration. As illustrated in Figure 8.19, RuvA tetramers recognize the Holliday junction, and RuvB uses the energy of ATP hydrolysis to unwind the parental duplexes and form heteroduplexes between them.

Figure 8.19: Branch migration of RuvA-RuvB in solution. The four monomers of RuvA combine around a central pen to accommodate the square planar configuration of the Holliday junction in which the four DNA duplex arms attach to grooves on the concave surface of RuvA. Through ATP hydrolysis, the two hexameric RuvB rings encircle and translocate the dsDNA arms. Curved arrows indicate rotation of DNA while the thick arrows indicate translocation of dsDNA through the junction. DNA rotation during Holliday junction branch occurs at a V(max) of 1.6 revolutions per second, or 8.3 bp per second. Adopted from reference Eggleston, A. K. and West, S. C. (1996) Trends in Genetics 12: 20-25. (Public Domain).


Bats save energy by reducing energetically costly immune functions during annual migration

Both seasonal migration and the maintenance and use of an effective immune system come with substantial metabolic costs and are responsible for high levels of oxidative stress. How do animals cope in a situation when energy is limited and both costly body functions are needed? A team of scientists led by the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) investigated whether and how the immune response changes between pre-migration and migration seasons in the Nathusius pipistrelle bat. They confirmed that migratory bats favour the energetically "cheaper" non-cellular (humoral) immunity during an immune challenge and selectively suppress cellular immune responses. Thereby, bats save energy much needed for their annual migration. The results are published in the scientific journal Scientific Reports.

The team of scientists around Christian C. Voigt, head of the Department of Evolutionary Ecology of the Leibniz-IZW, and Gábor Á. Czirják, senior scientist at the Department of Wildlife Diseases of the Leibniz-IZW, assessed the activity of several branches of the immune system of the Nathusius pipistrelle bat before and during migration. The seasonal journey of a 7 g Nathusius pipistrelle is energy-intensive since they fly more than 2.000 km during their annual journeys between the Baltic countries and southern France, and the metabolic turnover during flying is an order of magnitude higher than the basal metabolic rate . "It seems likely that bats will have to trade some body functions such as the immune response against the high cost of flight during migration", Voigt says. In order to verify this conjecture and to elucidate how the immune system is configured during this pivotal time of the year, the team measured the cellular and humoral response of the innate immune system (relative neutrophil numbers and haptoglobin concentration, respectively) and the cellular response of adaptive immunity (relative lymphocyte numbers) before and during migration. They compared baseline levels of these immune parameters and studied them in response to an antigen challenge.

"Our results confirm significant differences between the two periods. We conclude that this species of bat pays attention to the energy requirements of the different branches of immunity when switching from pre-migratory to the migratory season", Voigt explains. Before migration the cellular response of the innate immune response was significantly higher than during migration, whereas the humoral response of the same immune branch was dominant during the migration period. "The Nathusius pipistrelle responds with a strong humoral immune response to a challenge mimicking a bacterial infection. This response is more pronounced during migration, while there is no activation of the cellular response in such a situation", adds Czirják. When the animals embark on their strenuous journeys they reduce the cellular immune response, which is more energy-demanding than the humoral response. With this strategy the Nathusius pipistrelle might save energy during migration.

"The open question is whether or not the focus on humoral immunity during the migration period puts bats at some risk", Voigt says. "It is possible that they are more susceptible to certain pathogens while migrating if bats cannot mount an adequate cellular immune response." These and other related questions are now the topic of further immunological research by the bat research group at the Leibniz-IZW.

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Contents

Haplogroup L3 arose close to 70,000 years ago, near the time of the recent out-of-Africa event. This dispersal originated in East Africa and expanded to West Asia, and further to South and Southeast Asia in the course of a few millennia, and some research suggests that L3 participated in this migration out of Africa. A 2007 estimate for the age of L3 suggested a range of 104–84,000 years ago. [8] More recent analyses, including Soares et al. (2012) arrive at a more recent date, of roughly 70–60,000 years ago. Soares et al. also suggest that L3 most likely expanded from East Africa into Eurasia sometime around 65–55,000 years ago years ago as part of the recent out-of-Africa event, as well as from East Africa into Central Africa from 60–35,000 years ago. [1] In 2016, Soares et al. again suggested that haplogroup L3 emerged in East Africa, leading to the Out-of-Africa migration, around 70-60,000 years ago. [9]

Haplogroups L6 and L4 form sister clades of L3 which arose in East Africa at roughly the same time but which did not participate in the out-of-Africa migration. The ancestral clade L3'4'6 has been estimated at roughly 110 kya, and the L3'4 clade at 95 kya. [6]

The possibility of an origin of L3 in Asia was also proposed by Cabrera et al. (2018) based on the similar coalescence dates of L3 and its Eurasian-distributed M and N derivative clades (ca. 70 kya), the distant location in Southeast Asia of the oldest known subclades of M and N, and the comparable age of the paternal haplogroup DE. According to this hypothesis, after an initial out-of-Africa migration of bearers of pre-L3 (L3'4*) around 125 kya, there would have been a back-migration of females carrying L3 from Eurasia to East Africa sometime after 70 kya. The hypothesis suggests that this back-migration is aligned with bearers of paternal haplogroup E, which it also proposes to have originated in Eurasia. These new Eurasian lineages are then suggested to have largely replaced the old autochthonous male and female North-East African lineages. [4]

According to other research, though earlier migrations out of Africa of anatomically modern humans occurred, current Eurasian populations descend instead from a later migration from Africa dated between about 65,000 and 50,000 years ago (associated with the migration out of L3). [10] [2] [11]

Vai et al. (2019) suggest, from a newly-discovered old and deeply-rooted branch of maternal haplogroup N found in early Neolithic North African remains, that haplogroup L3 originated in East Africa between 70,000-60,000 years ago, and both spread within Africa and left Africa as part of the Out-of-Africa migration, with haplogroup N diverging from it soon after (between 65,000-50,000 years ago) either in Arabia or possibly North Africa, and haplogroup M originating in the Middle East around the same time as N. [2]

A study by Lipson et al. (2019) analyzing remains from the Cameroonian site of Shum Laka found them to be more similar to modern-day Pygmy peoples than to West Africans, and suggests that several other groups (including the ancestors of West Africans, East Africans and the ancestors of non-Africans) commonly derived from a human population originating in East Africa between about 80,000-60,000 years ago, which they suggest was also the source and origin zone of haplogroup L3 around 70,000 years ago. [12]

L3 is common in Northeast Africa and some other parts of East Africa, [13] in contrast to others parts of Africa where the haplogroups L1 and L2 represent around two thirds of mtDNA lineages. [14] L3 sublineages are also frequent in the Arabian peninsula.

L3 is subdivided into several clades, two of which spawned the macrohaplogroups M and N that are today carried by most people outside Africa. [14] There is at least one relatively deep non-M, non-N clade of L3 outside Africa, L3f1b6, which is found at a frequency of 1% in Asturias, Spain. It diverged from African L3 lineages at least 10,000 years ago. [15]

According to Maca-Meyer et al. (2001), "L3 is more related to Eurasian haplogroups than to the most divergent African clusters L1 and L2". [16] L3 is the haplogroup from which all modern humans outside Africa derive. [17] However, there is a greater diversity of major L3 branches within Africa than outside of it, the two major non-African branches being the L3 offshoots M and N.

Subclade distribution Edit

L3 has seven equidistant descendants: L3a, L3b'f, L3c'd, L3e'i'k'x, L3h, M, N. Five are African, while two are associated with the Out of Africa event.

  • N – Eurasia possibly due to migration from Africa, and North Africa possibly due to back-migration from Eurasia. [7][18][2]
  • M – Asia, the Mediterranean Basin, and parts of Africa due to back-migration. [7][18]
  • L3a – East Africa. [6][7] Moderate to high frequencies found among the Sanye, Samburu, Iraqw, Yaaku, El-Molo and other minor indigenous populations from the East African Rift Valley. It is infrequent to nonexistent in Sudan and the Sahel zone. [19]
    • L3a1 – Found across Eastern Africa. Estimated age of 35.8–39.3 ka. [7]
    • L3a2 – Found across Eastern Africa. Estimated age of 48.3–57.7 ka. [20][Note 1]
    • L3b – Spread from East Africa in the upper paleolithic to West-Central Africa. Some subclades spread from Central Africa to East Africa with the Bantu migration. [7]
      • L3b1a – Common subclade. Estimated age of 11.7-14.8 ka. [7]
        • L3b1a2 – Subclade found in Northeast Africa, the Maghreb, and Middle East. Emerged 12–14 ka. [21][20]
        • L3f1
          • L3f1a – Carried by migrants from Eastern Africa into the Sahel and Central Africa. [7]
          • L3f1b – Carried by migrants from Eastern Africa into the Sahel and Central Africa. [7]
            • L3f1b1 – Carried from Central Africa into Southern and Eastern Africa with the Bantu migration. [7]
              • L3f1b1a – Settled from East-Central Africa to Central-West Africa and into North Africa and Berber regions. [7]
              • L3c – Extremely rare lineage with only two samples found so far in Eastern Africa and the Near East. [7]
              • L3d – Spread from East Africa in the upper paleolithic to Central Africa. Some subclades spread to East Africa with the Bantu migration. [7] Found among the Fulani, [6]Chadians, [6]Ethiopians, [24]Akan people, [25]Mozambique, [24]Yemenites, [24]Egyptians, Berbers[26]
                • L3d3a1 – Primarily found in Southern Africa. [20][21]
                • L3e – Spread from East Africa in the upper paleolithic to West-Central Africa. It is the most common L3 sub-clade in Bantu-speaking populations. [27] L3e is suggested to be associated with a Central African origin and is also the most common L3 subclade amongst African Americans, Afro-Brazilians and Caribbeans[28]
                  • L3e1 – Spread from West-Central Africa to Southwest Africa with the Bantu migration. Found in Angola (6.8%). [29] Mozambique, Sudanese and Kikuyu from Kenya as well as in Yemen and among the Akan people[25]
                  • L3e5 – Originated in the Chad Basin. Found in Algeria, [30] as well as Burkina Faso, Nigeria, South Tunisia, South Morocco and Egypt[31]
                  • L3i1
                    • L3i1b – Subclade is found in Yemen, Ethiopia, and among Gujarati Indians. [21]
                    • L3h1 – Primarily found in East Africa with branches of L3h1b1 sporadically found in the Sahel and North Africa. [20][21]
                    • L3h2 – Found in Northeast Africa and Socotra. Split from other L3h branches as early as 65–69 ka during the middle paleolithic. [20][21]

                    Ancient and historic samples Edit

                    Haplogroup L3 has been observed in an ancient fossil belonging to the Pre-Pottery Neolithic B culture. [33] L3x2a was observed in a 4,500 year old hunter-gather excavated in Mota, Ethiopia, with the ancient fossil found to be most closely related to modern Southwest Ethiopian populations. [34] [35] Haplogroup L3 has also been found among ancient Egyptian mummies (1/90 1%) excavated at the Abusir el-Meleq archaeological site in Middle Egypt , with the rest deriving from Eurasian subclades , which date from the Pre-Ptolemaic/late New Kingdom and Ptolemaic periods. The Ancient Egyptian mummies bore Near eastern genomic component most closely related to modern near easterners. [36] Additionally, haplogroup L3 has been observed in ancient Guanche fossils excavated in Gran Canaria and Tenerife on the Canary Islands, which have been radiocarbon-dated to between the 7th and 11th centuries CE. All of the clade-bearing individuals were inhumed at the Gran Canaria site, with most of these specimens found to belong to the L3b1a subclade (3/4 75%) with the rest from both islands (8/11 72%) deriving from Eurasian subclades. The Guanche skeletons also bore an autochthonous Maghrebi genomic component that peaks among modern Berbers, which suggests that they originated from ancestral Berber populations inhabiting northwestern Affoundnat a high ncy [37]

                    A variety of L3 have been uncovered in ancient remains associated with the Pastoral Neolithic and Pastoral Iron Age of East Africa. [38]

                    Culture Genetic cluster or affinity Country Site Date Maternal Haplogroup Paternal Haplogroup Source
                    Early pastoral PN Kenya Prettejohn's Gully (GsJi11) 4060–3860 L3f1b Prendergast 2019
                    Pastoral Neolithic PN Kenya Cole’s Burial (GrJj5a) 3350–3180 L3i2 E-V32 Prendergast 2019
                    Pastoral Neolithic or Elmenteitan PN Kenya Rigo Cave (GrJh3) 2710–2380 L3f E-M293 Prendergast 2019
                    Pastoral Neolithic PN Kenya Naishi Rockshelter 2750–2500 L3x1a E-V1515 (prob. E-M293) Prendergast 2019
                    Pastoral Neolithic PN Tanzania Gishimangeda Cave 2490–2350 L3x1 Prendergast 2019
                    Pastoral Neolithic PN Kenya Naivasha Burial Site 2350–2210 L3h1a1 E-M293 Prendergast 2019
                    Pastoral Neolithic PN Kenya Naivasha Burial Site 2320–2150 L3x1a E-M293 Prendergast 2019
                    Pastoral Neolithic PN Tanzania Gishimangeda Cave 2150–2020 L3i2 E-M293 Prendergast 2019
                    Pastoral Neolithic or Elmenteitan PN Kenya Njoro River Cave II 2110–1930 L3h1a2a1 Prendergast 2019
                    Pastoral Neolithic N/A Tanzania Gishimangeda Cave 2000–1900 L3h1a2a1 Prendergast 2019
                    Pastoral Neolithic PN Kenya Ol Kalou 1810–1620 L3d1d E-M293 Prendergast 2019
                    Pastoral Iron Age PIA Kenya Kisima Farm, C4 1060–940 L3h1a1 E-M75 (excl. M98) Prendergast 2019
                    Pastoral Iron Age PIA Kenya Emurua Ole Polos (GvJh122) 420–160 L3h1a1 E-M293 Prendergast 2019
                    Pastoral Iron Age PN outlier Kenya Kokurmatakore N/A L3a2a E-M35 (not E-M293) Prendergast 2019

                    This phylogenetic tree of haplogroup L3 subclades is based on the paper by Mannis van Oven and Manfred Kayser Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation [5] and subsequent published research. [39]


                    Introduction

                    How new phenotypes emerge and evolve in populations has been a long-standing question in evolutionary biology ( Darwin 1859 Mayr 1942 Schlichting 1986 Reznick and Ricklefs 2009 Harvey et al. 2019). Over the past decade, high-throughput sequencing has enabled integration of phylogenetic and mechanistic studies of phenotypic variation across populations, improving our understanding of microevolution ( Andrews et al. 2016 Harvey et al. 2019). In this study, we investigated the evolution of significant reproductive and metabolic variation observed in the wild tomato species Solanum habrochaites, found in Peruvian and Ecuadorian Andes. Using Restriction site Associated DNA Sequencing (RAD-seq) ( Miller et al. 2007 Baird et al. 2008) in conjunction with phenotyping of mating systems and defensive metabolites called acylsugars, we sought to characterize how this phenotypic variation was shaped by the evolutionary history of the species and the unique geography of the Andes.

                    Solanum habrochaites ( Knapp and Spooner 1999) is a phenotypically diverse species with a range from the upper reaches of the Atacama desert in southern Peru to the tropical forests of central Ecuador. Tracking the western slope of the Andean mountain range in the south, this species is generally found 1,000–3,000 m above sea level but also extends to sea level in central Ecuador. This species range overlaps with the tropical Andes biodiversity hotspot, home to a sixth of global plant life and >20,000 endemic plant species ( Myers et al. 2000 Hazzi et al. 2018). Understanding the origins of genetic and phenotypic diversity in S. habrochaites can thus provide molecular mechanistic insights into the origins of biological diversity in this region.

                    Previous studies in this species ( Gonzales-Vigil et al. 2012 Kim et al. 2012 Schilmiller et al. 2015 Fan et al. 2017) have demonstrated substantial variation in two trichome-localized compound classes—acylsugars and terpenes—that are important for defense against herbivores ( Weinhold and Baldwin 2011 Leckie et al. 2016). For example, S. habrochaites accessions were grouped into two chemotypic superclusters based on their acylsugar profiles—a “northern” supercluster that failed to add an acetyl (C2) group to the sucrose R2 position in acylsugars, and a “southern” supercluster that retained this activity ( Kim et al. 2012). This loss of C2 addition resulted from mutational inactivation of acylsugar acyltransferase 4 (ASAT4)—the final enzyme in the Solanum acylsugar biosynthetic pathway—occurring via different means. Another study demonstrated differential acylation between northern and southern accessions on the furanose ring of the acylsugar, which could be traced back to gene duplication, divergence and loss in ASAT3, an upstream enzyme in the pathway ( Schilmiller et al. 2015). However, demographic processes that influenced this evolution of acylsugar profiles remain unknown.

                    Solanum habrochaites is also an attractive system for the study of reproductive trait evolution, with extensive diversity both in mating system and in reproductive barriers that affect interpopulation and interspecific gene flow. Solanum habrochaites is predominantly an obligate outcrossing species due to gametophytic S-RNase-based self-incompatibility (SI) ( Mutschler and Liedl 1994 Peralta et al. 2008 Bedinger et al. 2011). In this type of SI, the S-locus encodes pistil-expressed S-RNases and pollen-expressed S-locus F-box proteins that determine the specificity of the SI interaction. In addition, other pistil-expressed (e.g., HT protein) and pollen-expressed (e.g., CUL1) factors that are not linked to the S-locus play a role in self pollen rejection (reviewed in Bedinger et al. 2017). SI is widespread in flowering plants, and acts to preserve genetic diversity and diminish inbreeding depression ( Stebbins 1957 Lande and Schemske 1985 Schemske and Lande 1985 Takayama and Isogai 2005 Igic et al. 2008). However, there may be a selective advantage for transitions to self-compatibility (SC) during the dispersal of species, since a single SC individual could conceivably colonize a novel environment in the absence of other individuals or pollinators ( Baker 1955 Stebbins 1957 Baker 1967 Pannell et al. 2015).

                    SC populations of S. habrochaites have been identified at the northern and southern species range margins ( Martin 1961 Rick et al. 1979) in Ecuador and southern Peru, respectively. These marginal SC populations represent independent SI to SC transitions ( Rick and Chetelat 1991). Groups of SC populations at the northern species margin exhibit diverse reproductive barriers acting at the individual, population, and species levels ( Martin 1961 Broz, Randle, et al. 2017). Previous work that combined reproductive trait data with sequence analysis of S-RNase alleles identified two distinct SC groups (SC-1 and SC-2), and suggests that SC has arisen at least twice at the northern margin ( Broz, Randle, et al. 2017).

                    The ancestral SI populations of S. habrochaites originated in northern/central Peru ( Rick et al. 1979 Peralta et al. 2008 Pease et al. 2016) and, as the species spread, it traversed the Amotape-Huancabamba Zone (AHZ)—a region of cordillera disruption bordered in the south by Río Chicama in Peru and in the north by Río Jubones in Ecuador. This geographical disruption—one of the lowest altitude regions in tropical Andes—was generated through repeated geological fragmentation and remodeling over millions of years ( Antonelli et al. 2009 Hoorn et al. 2010), creating a number of unique microhabitats and rare east-west passages for species movement in the Andes. The AHZ includes a floristically diverse region called the Huancabamba Depression (HD) in the central part of the AHZ, which contains the lowest point in the entire Peruvian Andes ( Weigend 2002). The HD has sometimes been referred to ( Richter et al. 2009)—controversially ( Weigend 2004 Mutke et al. 2014)—as a barrier to dispersal of some high altitudinal plant species due to its low-lying nature. The species diversity in the overall AHZ is 6-8 times higher than in the area adjacent to the AHZ ( Weigend 2002). With its highly variable microhabitats and low-lying geology, the AHZ also shows high rates of endemism for both plants and animals ( Berry 1982 Weigend 2002), and may have also influenced S. habrochaites evolution.

                    To determine how both SI to SC transitions and acylsugar diversification occurred in the context of S. habrochaites species range expansion, we first determined the species’ population structure using RAD-seq and studied patterns of gene flow between different populations. We identified additional independent SI to SC transitions at the northern species margin, which were also associated with evolution of new acylsugar phenotypes. Our results revealed that alleles that eventually led to fixation of these novel phenotypes in Ecuador first emerged in the AHZ during the northward migration of S. habrochaites from central Peru. In contrast, we found a greater impact of geographical distance in central/southern Peru in the production of locally isolated populations. This work underscores the critical role of ecogeography and of repeated evolution of SC in shaping biological diversity.


                    New branch added to European family tree

                    Agriculture was sweeping in from the Near East, bringing early farmers into contact with hunter-gatherers who had already been living in Europe for tens of thousands of years.

                    Genetic and archaeological research in the last 10 years has revealed that almost all present-day Europeans descend from the mixing of these two ancient populations. But it turns out that's not the full story.

                    Researchers at Harvard Medical School and the University of Tübingen in Germany have now documented a genetic contribution from a third ancestor: Ancient North Eurasians. This group appears to have contributed DNA to present-day Europeans as well as to the people who travelled across the Bering Strait into the Americas more than 15,000 years ago.

                    "Prior to this paper, the models we had for European ancestry were two-way mixtures. We show that there are three groups," said David Reich, professor of genetics at HMS and co-senior author of the study.

                    "This also explains the recently discovered genetic connection between Europeans and Native Americans," Reich added. "The same Ancient North Eurasian group contributed to both of them."

                    The research team also discovered that ancient Near Eastern farmers and their European descendants can trace much of their ancestry to a previously unknown, even older lineage called the Basal Eurasians.

                    The study is published Sept. 18 in Nature.

                    To probe the ongoing mystery of Europeans' heritage and their relationships to the rest of the world, the international research team--including co-senior author Johannes Krause, professor of archaeo- and paleogenetics at the University of Tübingen and co-director of the new Max Planck Institute for History and the Sciences in Jena, Germany--collected and sequenced the DNA of more than 2,300 present-day people from around the world and of nine ancient humans from Sweden, Luxembourg and Germany.

                    The ancient bones came from eight hunter-gatherers who lived about 8,000 years ago, before the arrival of farming, and one farmer from about 7,000 years ago.

                    The researchers also incorporated into their study genetic sequences previously gathered from ancient humans of the same time period, including early farmers such as Ötzi "the Iceman."

                    "There was a sharp genetic transition between the hunter-gatherers and the farmers, reflecting a major movement of new people into Europe from the Near East," said Reich.

                    Ancient North Eurasian DNA wasn't found in either the hunter-gatherers or the early farmers, suggesting the Ancient North Eurasians arrived in the area later, he said.

                    "Nearly all Europeans have ancestry from all three ancestral groups," said Iosif Lazaridis, a research fellow in genetics in Reich's lab and first author of the paper. "Differences between them are due to the relative proportions of ancestry. Northern Europeans have more hunter-gatherer ancestry--up to about 50 percent in Lithuanians--and Southern Europeans have more farmer ancestry."

                    Lazaridis added, "The Ancient North Eurasian ancestry is proportionally the smallest component everywhere in Europe, never more than 20 percent, but we find it in nearly every European group we've studied and also in populations from the Caucasus and Near East. A profound transformation must have taken place in West Eurasia" after farming arrived.

                    When this research was conducted, Ancient North Eurasians were a "ghost population"--an ancient group known only through the traces it left in the DNA of present-day people. Then, in January, a separate group of archaeologists found the physical remains of two Ancient North Eurasians in Siberia. Now, said Reich, "We can study how they're related to other populations."

                    The team was able to go only so far in its analysis because of the limited number of ancient DNA samples. Reich thinks there could easily be more than three ancient groups who contributed to today's European genetic profile.

                    He and his colleagues found that the three-way model doesn't tell the whole story for certain regions of Europe. Mediterranean groups such as the Maltese, as well as Ashkenazi Jews, had more Near East ancestry than anticipated, while far northeastern Europeans such as Finns and the Saami, as well as some northern Russians, had more East Asian ancestry in the mix.

                    The most surprising part of the project for Reich, however, was the discovery of the Basal Eurasians.

                    "This deep lineage of non-African ancestry branched off before all the other non-Africans branched off from one another," he said. "Before Australian Aborigines and New Guineans and South Indians and Native Americans and other indigenous hunter-gatherers split, they split from Basal Eurasians. This reconciled some contradictory pieces of information for us."

                    Next, the team wants to figure out when the Ancient North Eurasians arrived in Europe and to find ancient DNA from the Basal Eurasians.

                    "We are only starting to understand the complex genetic relationship of our ancestors," said co-author Krause. "Only more genetic data from ancient human remains will allow us to disentangle our prehistoric past."

                    "There are important open questions about how the present-day people of the world got to where they are," said Reich, who is a Howard Hughes Medical Investigator. "The traditional way geneticists study this is by analyzing present-day people, but this is very hard because present-day people reflect many layers of mixture and migration.

                    "Ancient DNA sequencing is a powerful technology that allows you to go back to the places and periods where important demographic events occurred," he said. "It's a great new opportunity to learn about human history."

                    This project was supported in part by the National Cancer Institute (HHSN26120080001E and NIH/NCI Intramural Research Program), National Institute of General Medical Sciences (GM100233 and GM40282), National Human Genome Research Institute (HG004120 and HG002385), an NIH Pioneer Award (8DP1ES022577-04), National Science Foundation (HOMINID awards BCS-1032255 and BCS-0827436 and grant OCI-1053575), Howard Hughes Medical Institute, German Research Foundation (DFG) (KR 4015/1-1), Carl-Zeiss Foundation, Baden Württemberg Foundation and the Max Planck Society.

                    Written by Stephanie Dutchen

                    Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.


                    Conclusions

                    Osteosarcoma continues to be a challenging cancer to treat, and there has been a notable lack of progress in survival statistics for this aggressive bone cancer. Progress has stalled in part due to the lack of knowledge of OS pathogenesis. Historically, the lack of understanding of cellular mediators involved in proliferation and invasion of OS impaired our ability to target those mediators. As a result, the same backbone of chemotherapy has continued to remain the primary treatment strategy. The overall 5-year event free survival of pediatric patients with metastatic OS has been poor at 30% [6]. Simultaneously, there has been an eruption of scientific research investigating signaling pathways that appear to play crucial roles in tumor survival and renewal capacity. Many of these signaling pathways appear to be susceptible to targeting with natural compounds. These natural compounds have the potential to target multiple aberrant pathways in OS. Numerous in vitro and in vivo studies have demonstrated that these phytochemicals can modulate the signal pathways of OS. These various phytochemicals have already demonstrated considerable efficacy in a variety of other cancer types. Given the extraordinary lack of progress seen in OS clinical trials that continue to use various combinations of cytotoxic chemotherapy, it is time we look closer at these targeted agents and natural compounds. We need to quickly elucidate their mechanisms of action and safety profiles to push them into larger clinical trials for upfront therapy, so that we can finally make substantial advancements in treating this aggressive cancer.


                    Materials and Methods

                    Ovine Infinium HD SNP BeadChip Data

                    The Ovine Infinium HD SNP BeadChip data consisted of SNP genotypes generated in this study and those already available, totaling 3,850 individuals from 111 domestic sheep populations (3,447 individuals, hereafter referred to as “data set I”) and 7 wild sheep species (403 individuals, including 15 argali, 16 Asiatic mouflon, 4 urial, 98 European mouflon, 80 snow sheep, 135 bighorn, and 55 thinhorn sheep). The data generated here included genotypes of 888 individuals from 63 domestic sheep populations genotyped using the Ovine Infinium HD SNP BeadChip. DNA samples of the genotyped individuals were provided by the contributors ( supplementary tables S1 and S2 , Supplementary Material online).

                    The already available data include genotypes of 2,684 individuals (2,490 domestic sheep, 2 Asiatic mouflon, 2 European mouflon, 135 bighorn sheep, and 55 thinhorn sheep) with Ovine Infinium HD SNP BeadChip and 278 individuals (69 domestic sheep, 14 Asiatic mouflon, 4 urial, 96 European mouflon, 15 argali, and 80 snow sheep supplementary tables S1 and S2 , Supplementary Material online) with the OvineSNP50 BeadChip. Summary information, including breed names, abbreviations, geographic origins, sampling sizes, and contributors is detailed in figure 1A, supplementary figure S1 and tables S1 and S2 , Supplementary Material online. Approximate coordinates of the geographic origins for the samples were provided by the contributors, or were assigned as the centroid of their known range area.

                    In domestic sheep, we selected 70 autochthonous populations genotyped with the Ovine Infinium HD SNP BeadChip (data set II) in the analyses of climatic selective pressures. To assess genomic introgression from wild relatives to sympatric domestic populations, 13 populations (n = 896) from Eastern and Central Asia, 13 populations (n = 176) from Western Asia, 34 populations (n = 608) from Southwestern Europe, and 9 populations (n = 284) from Russia selected from data set I were merged with argali (n = 15), Asiatic mouflon (n = 18), European mouflon (n = 98), and snow sheep (n = 80), respectively (hereafter named as data set III [832 individuals and 41,358 common SNPs], IV [194 individuals and 41,057 common SNPs], V[695 individuals and 39,443 common SNPs], and VI [298 individuals and 41,737 common SNPs]).

                    Climatic Data from 1961 to 2001

                    A total of 117 climatic and elevation parameters for the geographic origins of 70 worldwide autochthonous breeds ( supplementary table S14 , Supplementary Material online) were extracted from the global climate data set (http://www.cru.uea.ac.uk/data, last accessed September 24, 2020) of the Climatic Research Unit, Norwich ( New et al. 2002). Data collected over a period of 40 years from 1961 to 2001 included yearly and monthly trends of the following variables: mean diurnal temperature range in °C (DTR), number of days with ground frost (FRS), the coefficient of variation of monthly precipitation in percent (PRCV), precipitation in mm/month (PR), number of days with >0.1 mm rain per month (RDO), relative humidity in percentage (REH), percent of maximum possible sunshine (percent of day length, SUN), mean temperature in °C (TMP), and 10-m wind speed in m/s (WND) ( fig. 7B supplementary fig. S11 , Supplementary Material online). High-resolution global distributions of elevation and the environmental variables (yearly mean values), and the Spearman’s rho correlation coefficient among the elevation and climatic parameters are shown ( supplementary fig. S12 and table S15 , Supplementary Material online).

                    SNP Data Quality Control

                    We first merged the Ovine Infinium HD BeadChip data sets and updated the SNP physical positions based on the assembly Oar v.4.0 (http://www.ncbi.nlm.nih.gov/ assembly/GCF_ 000298735.2, last accessed September 24, 2020) using the program PLINK v1.09 ( Purcell et al. 2007). Owing to differences in the number of SNPs and samples in the data sets, we then implemented quality control for the data sets using various criteria in PLINK v.1.09 software ( supplementary table S3 , Supplementary Material online). After filtering, we identified 495,965 SNPs and 3,217 individuals from data set I for genomic diversity and selective sweep analyses, 53,279 SNPs and 3,217 individuals from data set I for population structure analysis, 519,176 SNPs and 1,156 individuals of the 70 autochthonous breeds from data set II for climatic association analyses, 36,395 SNPs and 832 individuals from data set III, 36,508 SNPs and 194 individuals from data set IV, 36,148 SNPs and 695 individuals from data set V, and 29,562 SNPs and 298 individuals from the data set VI for genomic introgression analysis ( supplementary table S3 , Supplementary Material online).

                    Whole-Genome Sequences and SNP Calling

                    Chromosome 2 in 88 whole-genome sequences from our other studies was included in the analyses, comprising 54 domestic and 34 wild sheep ( Deng et al. 2020 Li et al. 2020 Chen ZH, Xu YX, and Li MH , unpublished data). The domestic sheep samples represent eight pneumonia-resistant (24 individuals) and ten pneumonia-susceptible populations (30 individuals) of different geographic origins from Asia (including the Middle East), Africa, and Europe. The wild sheep samples represent four pneumonia-resistant (two argali, four urial, seven Asiatic mouflon, and one European mouflon) and three pneumonia-susceptible species (six bighorn, six thinhorn, and eight snow sheep). The record of resistance or susceptibility to pneumonia for the wild species and domestic populations was from well-documented literature ( supplementary table S12 , Supplementary Material online). All the samples were sequenced on the Illumina Hiseq X Ten sequencer to a depth of 20–30× with an average depth of 20.49×. We filtered the raw reads using three criteria: 1) reads with unidentified nucleotides (N) more than 10%, 2) reads having adapters, and 3) reads with a phred quality score <5 were excluded. Clean reads were used for further analysis.

                    Using the Burrows–Wheeler Aligner (BWA) v.0.7.17 MEM module ( Li and Durbin 2009) with the parameter “bwa –k 32 –M -R,” we mapped the clean reads onto the sheep reference genome Oar v.4.0. Duplicate reads were excluded using Picard MarkDuplicates and were sorted using Picard SortSam. We only kept reads that were properly mapped with mapping quality more than 20. Base quality score recalibrate (BQSR) with ApplyBQSR module of the Genome Analysis Toolkit (GATK) v.4.0 ( McKenna et al. 2010) was applied to eliminate potential sequence error. After mapping, SNP calling was carried out by GATK best practice workflow of joint genotyping strategy. Two steps were carried out: 1) We called variants for each sample using Haplotypecaller module with parameter “--genotyping-mode DISCOVERY --min-base-quality-score 20 --output-mode EMIT_ALL_ SITES --emit-ref-confidence GVCF” and 2) we performed joint genotyping by combining all the GVCFs using GenotypeGVCFs module and CombineGVCFs module together. Variant sites with QUAL <30.0, QD <2.0, FS >60.0, MQ <40.0, HaplotypeScore >13.0, MappingQualityRankSum <12.5, and ReadPosRankSum <8.0 were discarded using VariantFiltration module of GATK. We called SNP separately for each species and then merged them using BCFtools v1.9 ( Li 2011). PLINK v1.09 was used for further filtering of SNPs in the populations. We excluded SNPs that met any of the following criteria: 1) proportion of missing genotypes >10% and 2) SNPs with minor allele frequency <0.05. In total, we identified 119,884 SNPs on chromosome 2 for further analysis.

                    Genetic Diversity and Population Genetic Structure

                    We evaluated the genomic diversity for the populations of wild and domestic sheep based on seven metrics, including the proportion of polymorphic SNPs (Pn), observed (Ho) and expected heterozygosity (He), inbreeding coefficient (F) using PLINK v1.09, and allelic richness (Ar) and private allele richness (pAr) using ADZE v1.0 ( Szpiech et al. 2008). We investigated the levels of LD between pairs of autosomal SNPs with the r 2 estimate (the parameters “--r2 --ld-window 99999 --ld-window-r2 0”) using PLINK v1.09. Recent effective population size (Ne) for each population was then calculated based on the LD estimates (Kijas et al. 2012).

                    To examine population genetic structure, we first implemented PCA based on the SmartPCA program from the EIGENSOFT v.6.0beta ( Patterson et al. 2006). Additionally, we constructed an NJ tree based on pairwise Reynolds’ genetic distances ( Reynolds et al. 1983) using PHYLIP v.3.695 ( Felsenstein 1993). The NJ tree was visualized with FigTree v.1.4.3 ( Rambaut 2014), and the robustness of a specific tree topology was tested by 1,000 bootstraps. We explored the genetic components of the populations using the maximum-likelihood clustering program ADMIXTURE v1.23 with K = 2–10 ( Alexander et al. 2009). We then performed additional ADMIXTURE analyses for the domestic populations from different geographic regions selected in the genomic introgression analyses (see below) using the sympatric wild species as the outgroup (i.e., argali for 13 Eastern and Central Asia populations, Asiatic mouflon for 13 Western Asian populations, European mouflon for 34 Southwestern European populations, and snow sheep for 20 Russian populations). We processed the results with Clumpp v1.1.2 ( Jakobsson and Rosenberg 2007) and visualized them with Distruct v1.1 ( Rosenberg 2003).

                    Genomic Introgression

                    To determine the potential genomic introgressions from wild relatives into sympatric domestic breeds, such as argali to Eastern and Central Asian breeds, Asiatic mouflon to Western Asian breeds, European mouflon to Southwestern European breeds, and snow sheep to Russian breeds ( fig. 4A), we implemented three different statistical analyses in the selected populations of wild and domestic sheep ( supplementary table S9 , Supplementary Material online), including TreeMix ( Pickrell and Pritchard 2012), f3-statistics ( Patterson et al. 2012), and D-statistics ( Patterson et al. 2012). In the TreeMix analysis, we constructed the maximum likelihood trees using blocks of 1,000 SNPs and the wild species of argali, Asiatic mouflon, European mouflon, and snow sheep as the roots. We set the number of tested migration events (M) from 1 to 20 and quantified the model by the covariance for each migration event. Additionally, we computed f3-statistics using the qp3Pop program with the default parameters in the AdmixTools package ( Patterson et al. 2012). We used the f3-statistics based on the following scenarios: f3(C argali, B), f3(C Asiatic mouflon, B), f3(C European mouflon, B), and f3(C snow sheep, B), where B and C represent domestic breeds from the same geographic regions such as Eastern and Central Asia, Western Asia, Southwestern Europe, and Russia. We considered f3-statistics <0 as statistically significant, indicating historical events of admixture ( Reich et al. 2009). Also, we estimated D-statistics using the program AdmixTools ( Patterson et al. 2012) for the following scenarios: D (MZS, X, argali, BIGHORN), D (MZS, X, Asiatic mouflon, BIGHORN), D (MZS, X, European mouflon, BIGHORN), and D (MZS, X, snow sheep, BIGHORN), where MZS, X, and BIGHORN represent Menz sheep (the nonintrogressed reference population), domestic populations from the four geographic regions above, and the outgroup of bighorn sheep, respectively. |Z| scores >3 indicated statistically significant (P < 0.001) deviations from D = 0, suggesting gene flow occurred between the donor wild species and tested domestic population X.

                    Further, we characterized the introgressed genomic regions on the basis of local-ancestry assignment and CIWIs ( Barbato et al. 2017). We first phased the genotypes using the program fastPHASE v1.239 with the default parameters ( Scheet and Stephens 2006). For the focal domestic populations, we identified the genomic segments of the wild ancestry based on the reference of a wild and four domestic populations using the program PCAdmix v1.0 ( Brisbin et al. 2012). We further used a sliding window to identify the introgressed genomic segments following the method developed by Barbato et al. (2017). We filtered the results of highly concordant introgression signals along chromosomes and assigned a concordance score to each sliding window. We took the 95 th percentile of the genome-wide concordance score distribution as the CIWIs for each autosome.

                    Based on the results of TreeMix, Admixture, f3-statistics, D-statistics, and CIWI analyses, breeds showing introgression signals in both population-level and genomic-level analyses were selected as “introgressed populations,” whereas others showing no signals of introgression in either population-level or genomic-level analysis were considered as “nonintrogressed populations.” To examine the contribution of wild introgression to genomic diversity in domestic populations, we compared the genomic diversity estimates (e.g., He, Ho, and Pn) between the two groups of domestic populations (introgressed vs. nonintrogressed) using the Kruskal–Wallis test in the software SPSS v24.0 ( IBM Corp. 2016).

                    Whole-Genome Sequence Analyses and ILS

                    Furthermore, we focused on the introgressed region chr2: 245–249 Mb (especially the focal gray-shaded region: 248.28–248.4 Mb, 248.28–248.58 Mb, and 248.26–248.5 Mb in argali introgression, Asiatic mouflon introgression, and European mouflon introgression, respectively) that harbors the PADI2 gene using high-depth whole-genome sequences. We validated the introgressed signals detected above using ABBA, BABA, D-statistics, and f-statistic (fd) value ( Martin et al. 2015). We used the D-statistics [[[MEN, BSB], ARGS], BIGS], [[[MEN, GSS], AMUF], BIGS], and [[[MEN, CAUC], EMUF], BIGS], in which BIGS (bighorn sheep) is the outgroup ARGS (argali), AMUF (Asiatic mouflon), and EMUF (European mouflon) represent the donor species MEN (Menz sheep) and populations of BSB, GSS, and CAUC (Bashibai, Grey Shiraz, and Caucasian sheep) represent the domestic sheep susceptible and resistant to pneumonia, respectively. To further locate the introgressed genomic regions, we computed the f-statistic (fd) value for each 100-kb sliding window with 20-kb step across the whole genome of argali versus Bashibai sheep, Asiatic mouflon versus Grey Shiraz sheep, and European mouflon versus Caucasian sheep, respectively, with Menz sheep as the nonintrogressed reference population and bighorn sheep as the outgroup. We phased the genotypes of chromosome 2 using Shapeit v2.12 ( Delaneau et al. 2014).

                    Further, we calculated the mean pairwise sequence divergence (dxy) between argali and Menz/Bashibai, between Asiatic mouflon and Menz/Grey Shiraz, as well as between European mouflon and Menz/Caucasian. Also, the FST value of the common introgressed genomic region was calculated for the pairwise comparisons of argali vs. Bashibai, Asiatic mouflon vs. Grey Shiraz, and European mouflon vs. Caucasian. The introgressed tract could be due to ILS. To verify that the target genomic region was derived from introgression rather than common ancestry, we calculated the probability of ILS for the three tracks, which were inferred to be introgressed from argali to Bashibai, Asiatic mouflon to Grey Shiraz, and European mouflon to Caucasian sheep. The expected length of a shared ancestral sequence is L = 1/(r × t), in which r is the recombination rate per generation per base pair (bp), and t is the branch length between argali/mouflon and domestic sheep since divergence. The probability of a length of at least m is 1 − GammaCDF (m, shape = 2, r = 1/L), in which GammaCDF is the Gamma distribution function ( Huerta-Sánchez et al. 2014 Hu et al. 2019).

                    In addition, we constructed the NJ tree based on the p-distance for the target genomic region among the pneumonia-susceptible and pneumonia-resistant populations using PLINK v1.09 ( Purcell et al. 2007). To view the specific genotype pattern of the common introgressed gene PADI2, we extracted the genotypes of 435 SNPs in the PADI2 gene (chr2: 248,285,826–248,352,997) from the high-depth whole-genome sequences and compared the pattern of the genotypes between the pneumonia-susceptible (i.e., 3 pneumonia-susceptible wild species, 20 individuals 10 pneumonia-susceptible domestic breeds, 30 individuals) and pneumonia-resistant samples (i.e., 4 pneumonia-resistant wild species, 14 individuals 8 pneumonia-resistant domestic breeds, 24 individuals).

                    Signatures for Local Climatic Adaptation

                    We performed two different algorithms to detect signatures of local adaptation across the 70 worldwide autochthonous breeds ( supplementary table S14 , Supplementary Material online). First, we used an individual-based spatial analysis method ( Stucki et al. 2017) in the Samβada software (https://lasig.epfl.ch/sambada, last accessed September 24, 2020). The algorithm measured explanatory variables incorporating population structure into the models to decrease the occurrences of spurious genotype–environment associations. The Samβada calculated multiple univariate logistic regression analyses, whereby individuals were coded as either present or absent (i.e., binary information: 1 or 0) for a given SNP allele and the association between all possible pairs of allele and environmental variable was measured across the sampling sites. We considered the significant models based on the Wald tests with the Bonferroni correction at the level of P < 0.01.

                    We further calculated the correlations between SNPs and climatic variables using the LFMM (http://membres-timc.imag.fr/Olivier.Francois/lfmm/index.htm, last accessed September 24, 2020) program, which is based on population genetics, ecological modeling, and statistical learning techniques ( Frichot et al. 2013). The LFMM can efficiently control for random effects that represent population history and isolation-by-distance patterns and lower the risk of false-positive associations in landscape genomics ( Frichot et al. 2013). We summarized all the climatic variables by using the first axis from a PCA ( supplementary table S16 , Supplementary Material online). We identified K = 4 latent factors on the basis of population structure analyses using the programs SmartPCA and STRUCTURE v2.3.4 ( supplementary figs. S13 and S14 , Supplementary Material online). We computed LFMM parameters (|z| scores) for all the variants based on the MCMC (Markov chain Monte Carlo) algorithm with a burn-in of 5,000 and 10,000 iterations. The threshold for identifying candidate genes in LFMM analyses was set to |z| scores ≥10, indicating significant SNP effects at the level of P < 10 −22 after Bonferroni correction for a type I error α = 10 −16 and L ≈ 1 × 10 6 loci ( Frichot et al. 2013 Lv et al. 2014).

                    Haplotype, Selective Signature, and LD Analysis within PADI2

                    To examine the haplotypes in PADI2, we first excluded SNPs with proportion of missing genotypes >2% and minor allele frequency <0.01 in the 88 individuals with whole-genome sequences. Shapeit v2.12 ( Delaneau et al. 2014) was then used to infer the haplotypes. Furthermore, genome scan across chromosome 2 was performed to identify potential regions under positive selection between pneumonia-resistant (Bashibai, Caucasian, Grey Shiraz, Chinese fine-wool Merino, Djallonke, Kazakh, Awassi, and Suffolk) and pneumonia-susceptible (Thalli, Dorper, Kajli, Nellore, Mecheri, Afar, Afshari, Tibetan, Menz, and Karakul) breeds of domestic sheep using XP-CLR v.1.0 ( Chen et al. 2010). The SNPs with <10% missing data were allowed (--max-missing 0.9) in the analysis. XP-CLR scores were calculated using the grid points spaced by 2,000 bp with a maximum of 200 SNPs in a window of 0.5 cM and the down-weighting contributions of highly correlated variants (r 2 > 0.95) with the parameters -w1 0.005 200 2000 2 -p0 0.95. The top 1% genomic regions with the highest XP-CLR scores were considered to be the selective signatures. In addition, the sliding-window approach (window size = 1 kb) was used to quantify the Tajima’s D of pneumonia-resistant and pneumonia-susceptible populations using vcftools v0.1.14 ( Danecek et al. 2011). Also, we implemented genome-wide scans of adaptive introgression using VolcanoFinder v1.0 ( Setter et al. 2020). We first generated the allele frequency file and the unnormalized site frequency spectrum using SweepFinder2 ( DeGiorgio et al. 2016). The two files were then used as input in the program VolcanoFinder with the options of ./VolcanoFinder –ig 1000 and the Model 1. Haploview software ( Barrett et al. 2005) was then used to examine the LD pattern in PADI2.

                    Gene Annotation, GO, and Pathway Analysis

                    We annotated genes located within 20 kb upstream and downstream of these selective regions or SNPs using the O. aries assembly Oar_v.4.0 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000298735.2, last accessed September 24, 2020). We implemented GO enrichment and KEGG pathway analyses for the annotated genes using the sheep genome background in the DAVID (database for annotation, visualization, and integrated discovery) Bioinformatics Resources v.6.8 ( Huang et al. 2009). The threshold was set to P < 0.05 and at least two genes from the input gene list in the enriched category were considered as the enriched GO terms and KEGG pathways. We created word clouds for the significance GO terms and KEGG pathways on the Wordcloud generator (https://www.jasondavies.com/wordcloud/, last accessed September 24, 2020).


                    Impossible to scan subtitles : errors in .py files since the migration to python 3.5 #7346

                    A few months ago I migrated to Python 3.5.
                    Since it is impossible for me to scan the subtitles available on any provider.
                    I receive the error messages below.

                    Medusa (please complete the following information):

                    • OS: Linux-3.14.32-xxxx-grs-ipv6-64-x86_64-with-debian-8.11
                    • Branch: master
                    • Commit: 80b323e
                    • Python version: 3.5.0 (default, Apr 15 2019, 23:23:34) [GCC 4.9.2]
                    • Database version: 44.14

                    The text was updated successfully, but these errors were encountered:

                    We are unable to convert the task to an issue at this time. Please try again.

                    The issue was successfully created but we are unable to update the comment at this time.

                    Medariox commented Nov 13, 2019 •

                    1. Delete the subliminal.dbm* files in your cache folder.
                    2. Disable addic7ed provider as it is not working since quite some time and probably can't be fixed, see: Diaoul/subliminal#966 and #7264

                    Tomzic59 commented Nov 13, 2019

                    I did not know about Addi7ed.
                    I deleted the cache subliminal.dbm file, then restarted medusa service.
                    But I am still getting the error in logs and can not scan subtitles.

                    P0psicles commented Nov 14, 2019

                    Tomzic59 commented Nov 14, 2019 •

                    Yes I disabled addic7ed in "Subtitles Settings", so i do not receive any any errors about it.
                    But when I run a subtitle scan for an episode, I continue to receive this:

                    Medariox commented Nov 14, 2019

                    You need to shut down Medusa first.
                    Delete all the files that start with subliminal in your cache folder.
                    Start Medusa again.

                    We already had this report before and everyone was able to solve the issue by doing this.

                    Tomzic59 commented Nov 14, 2019

                    Mine seems to be more persistent.
                    I did what you said, but I always get the same error when I do a manual scan.

                    Rouzax commented Dec 28, 2019

                    P0psicles commented Dec 28, 2019

                    I think that when they release their api, it wouldn't take long before it's added to subliminal or we add it as external provider.

                    Forceflow commented Jan 3, 2020 •

                    Fwiw, I've been having good results using https://pypi.org/project/addic7ed-cli/ for automated / scripted subtitle grabbing from Addic7ed. It's in pypi, works on Python3 and was sort of hassle-free.

                    addic7ed -l eng --lang-suffix -i -bb <filename here> gets you a language-suffixed, best-result (if any) srt file without any user interaction.

                    You can manually add a PHP session ID to the config file to get "logged in" functionality (still a limit of 40 downloads / 24hrs).

                    P0psicles commented Jan 3, 2020

                    That would require allot of hacking to get that integrated. Then I'd prefer for them to have added the api. So we can add it as a subtitle provider

                    Forceflow commented Jan 7, 2020

                    That would require allot of hacking to get that integrated. Then I'd prefer for them to have added the api. So we can add it as a subtitle provider

                    Fair enough - do we have an ETA on the addic7ed API?

                    I'm going to try and add this addic7ed script as a post-processing step using Medusa's existing capabilities. The subliminal dev tree (which I suppose Medusa's also using?) is failing for addic7ed (expected) and sometimes for opensubtitles for me, so some additional attempts to get subtitles must be added :)


                    INTRODUCTION

                    The first permanent structure in sea urchin development is the single cilium assembled by each blastomere before hatching. During ciliogenesis, the synthesis of tubulin and dynein, already present in large pools, is up-regulated. Most 9+2 architectural proteins are made in discrete, lesser amounts. Some of these, such as the integral outer doublet microtubule component tektin-A, are synthesized in limited, length-proportionate amounts, a process referred to as quantal synthesis (Stephens, 1977, 1989). The synthesis of all of these building blocks is up-regulated in response to hypertonic deciliation: the larger pools of tubulin and dynein and the smaller pools of most architectural proteins are replenished while another quantal amount of the length-limiting proteins is made. Since the process of regeneration recapitulates both the morphologic and the synthetic events characteristic of ciliogenesis without altering the progress of development — regardless of how often embryos are induced to regenerate cilia —ciliogenesis has been referred to as a subroutine in the program of development by analogy to the computer programming counterpart (Stephens, 1995a). As such, it is a highly convenient system with which to study inducible gene expression and organelle assembly.

                    Complicating this simple reiterative theme is the fact that ciliary proteins, after assembly, can be labeled to a level approaching that of full regeneration (Stephens, 1991, 1994a). This background synthesis is reflected in steady-state, ciliary length-related levels of tektin mRNAs (Norrander et al., 1995). Little protein labeling can be attributed to ciliary growth (Stephens, 1994a). Furthermore, steady-state turnover is not metabolic since the ciliary protein pools do not lose significant label in the presence of unlabeled amino acid (Stephens, 1989).

                    Tubulin pool size has long been considered a potential controlling factor in ciliary growth. Since the synthesis of 9+2 proteins appears to be under coordinate control during regeneration and continues at a reduced rate after assembly, it would be useful to document these processes quantitatively and determine whether the pool of available tubulin or its synthesis influences either the stoichiometric synthesis of other ciliary proteins (e.g., feed-back) or the turnover/exchange process after growth (e.g., cotransport). It is well established that colchicine leads to the depolymerization of cytoplasmic microtubules while taxol favors their assembly. Colchicine quite effectively prevents the assembly of sea urchin embryonic cilia but, once formed, the cilia are not structurally responsive to the drug (Tilney and Gibbins, 1968). As a consequence of autoregulation mediated by an increased amount of free tubulin, the synthesis of tubulin can be inhibited selectively by treating sea urchin embryos with colchicine (Gong and Brandhorst, 1988). Furthermore, taxol, which does not inhibit tubulin synthesis, can be used to decrease the free tubulin concentration and stabilize existing microtubules. Thus it is feasible to inhibit specifically both tubulin synthesis and assembly or to vary the effective tubulin pool size while retaining fully-functional cilia.

                    This study investigates ciliary protein synthesis and turnover with respect to several related questions. What are the baseline quantitative synthetic relationships among the 9+2 proteins in multiple regenerations and during steady-state turnover? Does turnover occur when tubulin synthesis, assembly, or effective pool size is modulated by tubulin-specific inhibitors? If so, are the architectural proteins of the cilium still proportionately synthesized? This approach also provides a rigorous assessment of the degree of protein turnover when ciliary regeneration and elongation are blocked. The recent discovery of a molecular chaperone concentrated at the distal growth tip ofChlamydomonas flagella (Bloch and Johnson, 1995) prompted a search for its equivalent in embryonic cilia. Is a chaperone associated with the 9+2 axoneme at steady state or during regrowth and does blockage of tubulin assembly or disassembly influence the amount or distribution of this potential mediator of transport and assembly? Brief accounts of this work have been presented in meeting proceedings (Stephens, 1994b, 1995b).


                    ACKNOWLEDGMENTS

                    Our field efforts in the Antarctic Peninsula area were supported by Lindblad Expeditions - National Geographic Conservation Fund, and the BBC Natural History Unit. Jerome and Dion Poncet provided shipboard support S. Martin, J. Kelley, L. Kelley, and L. Ballance provided field assistance. Our work in McMurdo Sound was supported by the National Science Foundation under grants to R. Pitman (OPP-0338428) and S. Kim (ANT-0944747), and from the International Whaling Commission/Southern Ocean Research Partnership. We would like to acknowledge the enthusiastic support of the staff at McMurdo Station, especially the helicopter pilots and the ice safety crew for field assistance, we thank D. Mahon, D. Baetscher, and S. Kim. Research was conducted under ACA Permit 2009-013 IACUC Permits SWPI2010-02, SWPI2013-03, and SPWI2017-01 and MMPA Permits 774-1714 and 14097 issued to NOAA Fisheries, Southwest Fisheries Science Center. The research at Terra Nova Bay was supported by an Italian Antarctic Research Programme grant to G. Lauriano (2013/AZ1.08). We thank the staff at Mario Zucchelli Station, and the helicopter pilots and ice safety crew for the assistance during the research in the field. This manuscript was improved by comments from P. Clapham, L. Ballance, S. Mesnick, and an anonymous reviewer Figure 1 was prepared by the late A. Dahood.


                    Watch the video: Οι πρώτοι Έλληνες μετανάστες #vlog8 (May 2022).