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Torrent tracker list 2015 october taurus

torrent tracker list 2015 october taurus

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Federal government websites often end in. The site is secure. Previous analyses have indicated that environmental triggers associated with weather conditions, specifically air moisture and temperature in the region of the saiga antelope calving during the day period running up to the event, were critical to the proliferation of latent bacteria and were comparable to conditions accompanying historically similar die-offs in the same areas.

We investigated whether additional viral or bacterial pathogens could be detected in samples from affected animals using 3 different high-throughput sequencing approaches. We did not identify pathogens associated with commensal bacterial opportunisms in blood, kidney, or lung samples and thus concluded that P. The saiga antelope Saiga tatarica tatarica and S.

Each year during the month of May, Saiga antelopes gather in Kazakhstan for calving. Mass die-offs in their populations have been reported previously and were attributed to viral and bacterial etiologies, including pasteurellosis 2. However, the diagnosis in most of these events has been unreliable because of insufficient fresh sampling and diagnostic work 2. During a large outbreak in , extensive diagnostics and environmental studies were undertaken, subject to restricting factors such as remoteness and limited cold chain resources.

Annual disease monitoring in saiga antelopes had been established after die-offs occurred in western Kazakhstan in , and an international multidisciplinary research team was on the ground at the time of the die-off, performing routine surveillance 3 , 4. These subgroups of saiga antelopes were spread discretely across a landscape of several hundreds of thousands of square kilometers.

The number of dead animals constituted more than two thirds of the global population of saiga antelope at the time. Laboratory results on the microbiologic, pathologic, and environmental conditions at the time of the outbreak suggested hemorrhagic septicemia caused by Pasteurella multocida serotype B and triggered by environmental conditions 3 , 6. However, whether a second unknown infectious agent had predisposed the animals to infection with P.

Given the opportunistic nature of Pasteurella , the objective of our study was to attempt to identify whether any additional unknown potential causative pathogens were present in samples taken from 10 animals that might may have contributed to the die-off. The first dead animals were detected in the Amangeldy District Kostanay region of Kazakhstan on May 10, , and additional die-offs were recorded in unconnected discrete locations in the Aktobe and Akmola regions 3.

A primary diagnosis of hemorrhagic septicemia as the cause of death was proposed at the sites on the basis of clinical signs and gross pathology. We took FTA papers of whole blood spots from 8 freshly dead, female animals Table 1 in a 2-km radius on the last 2 days of the operation and sent them to international reference laboratories for high-throughput sequencing HTS protocols.

FTA cards were used as backup given the limited resources available and difficulties in maintaining cold chain and in transportation of fresh samples to local laboratories. Lung and kidney tissue from 2 dead saiga antelopes lung tissue from animal X and kidney tissue from animal Y from the Turgai River region were also processed for 16S metagenomics sequencing in the city of Almaty, Kazakhstan. Although these samples were from a region km from the site where the FTA card samples were taken, they were considered part of the same saiga antelope population.

Given the uniformity of the clinical syndrome and consistency of the pathogenesis, the sample of cases selected was small relative to the scale of the die-off, but each case was evaluated in considerable depth and considered representative of the affected population on the basis of the consistent pathology and disease characteristics observed in all the affected animals 3.

Two of the 8 samples were processed by both laboratories. Lung and kidney tissue from 2 dead saiga antelopes Table 2 were tested for 16S bacterial diversity by using a 16S metagenomic sequencing protocol developed by the Institute of Microbiology and Virology in Almaty Figure 2.

Geographic distribution of saiga antelope die-off events, Kazakhstan, Red and orange areas indicate known outbreak locations of the 3 saiga populations. Inset shows area in relation to the rest of Kazakhstan and neighboring countries of central Asia.

Outline of the process of sampling and high-throughput sequencing protocols performed at 3 research institutes in an investigation of a mass die-off of saiga antelopes, Kazakhstan, We analyzed reads from each of the parallel investigations by using established bioinformatics pipelines to identify microbial agents present within each sample. All raw datasets, de novo assemblies, and 16S sequencing metagenome datasets have been submitted to the European Nucleotide Archive and GenBank accession nos.

The classification of sequenced reads into different taxonomic groups was conducted by using 2 approaches; the first was a k-mer—based approach that assigned each read independently Table 3 ; Appendix Table 1 , and the second was a de novo approach that first assembled reads into contigs and then assigned contigs Table 4 ; Appendix Tables 2—4.

Neither of these 2 approaches conclusively identified a single virus as a causative agent in all samples. In all 6 samples tested, The specificity of this finding was increased for the P. QC, quality control. The de novo analysis approach also did not identify any homologies with unexpected viral genomes. Several bacteria were identified by both k-mer and de novo analysis protocols, including P.

We subjected these contigs to an extended analysis in which they were first aligned to BLAST databases with tblastx to find similarities at the protein level. That analysis generated matches for 35 contigs; the distribution of the matches in terms of species mirrors quite closely the one found by nucleotide BLAST Appendix Tables 2—4. This approach returned hits for 87 contigs Appendix Tables 2—4 , of which most appeared to be homologs of bacterial proteins.

No further pathogens could be conclusively identified using this analysis. Further analysis of the assembled sequences attributed to P. By using the RIEMS analysis pipeline, we performed taxonomic analysis of the sequencing reads obtained from libraries generated from RNA extracted from 4 blood spots from FTA cards that had been transcribed into cDNA using random hexamer priming followed by shotgun library preparation.

Of the samples tested at FLI, samples 2 and 5 were also tested at Pirbright. Overall, these analyses classified The remainder mainly represented host sequences 0. With a few exceptions for phage reads, no reads were classified as being of viral origin, which was concordant with findings of the Pirbright dataset.

In all samples, the proportion of reads remaining unclassified after analysis of the nucleic acid sequences was low 0. Therefore, the information content of the unclassified portion of the datasets was too low to provide additional information even by additional analyses on the basis of the amino acid sequences deduced from these reads. To conduct a detailed analysis of the numerous P.

We then performed blastx 7 analyses of the resulting contigs for a basic function prediction of the expressed genes. Besides detecting genes encoding proteins of gene expression, general metabolism, and cell division, these analyses detected several proteins associated with pathogenicity. For example, proteins facilitating active iron uptake iron ABC transporter permease [GenBank accession no. These analyses also revealed expression of genes encoding stress- and starvation-induced proteins stringent starvation protein A homologue [accession no.

Among the variable regions of 16S gene, V3 is a highly variable region that can distinguish bacteria to the genus level. V4 is also efficient but less so than V3 8. The output of the workflow classified the reads at the primary taxonomic levels kingdom, phylum, class, order, family, genus, and species. Sequencing statistics revealed the number of total reads to be 63, for lung tissue and 15, for kidney tissue.

The number of reads passing quality filtering was 58, for lung tissue and 14, for kidney tissue. The percentage of reads passing quality filtering was Of all reads generated, Other species were Pasteurellaceae Saiga antelopes are a critically endangered species 1 , and the population is increasingly fragmented and vulnerable to stochastic events such as disease epidemics. The saiga antelopes undertake large-scale seasonal migrations between their summer and winter ranges because of the extreme variation in climate conditions and the need for pastures offering sufficient forage.

The calving sites are highly variable from year to year and depend on plant phenology, environmental factors, and anthropogenic effects 9. A few incidences of infectious disease, including foot-and-mouth disease, have been confirmed 12 , but most events were attributable to pasteurellosis; M. However, diagnoses are lacking comprehensive clinical, pathological, epidemiologic, and environmental investigation and remain tentative in all cases outside the event.

Diagnosis of wildlife deaths is constrained by the fact these populations are not managed nor always monitored regularly, meaning die-offs occur frequently and investigators often do not have access to fresh carcasses. In the saiga antelope event, a monitoring team was in place in 2 of the 15 die-off locations and were equipped for general diagnostic work.

This situation was unusual and provided a unique opportunity, but the unpredictability of such an event happening limited the extent of the outbreak investigation. Sampling was necessarily strategic, and because all of the animals in the population were affected by the same syndrome and died, the sample size did not need to be large or statistically representative.

Each case would have an equal chance of providing the result, and failure to diagnose would be more likely a product of insufficient material per case or loss of viability of organisms because of cold chain and storage issues. Nevertheless, the findings obtained from this work are representative of the population for a few reasons.

First, the clinical syndrome was uniform in both the adult and calf populations. We observed no statistically significant variation in the temporal progression once symptoms were noted, and clinical signs and gross pathology were highly consistent. In addition, the rapidity of the syndrome precluded large numbers of cases being investigated by our relatively small team because necropsy and sampling for each case took several hours to complete.

Previous studies had demonstrated the absence of other potential causative agents by diagnostic PCR, including bacteria e. Furthermore, these studies used capsular typing with specific primers to show that strains of P. Our 16S analysis also showed that the P. Each of these workflows demonstrated the potential for different experimental challenges in obtaining metagenomic datasets e.

The high sensitivity of such methods to detect small amounts of nucleic acids also poses challenges in terms of prevention of contamination and false-positive results. Caution should be exercised in drawing conclusions from such datasets without appropriate validation.

In addition to blood spots, other tests, including bacteriologic and virologic tests on various tissues and samples taken, were conducted locally in Kazakhstan at government laboratories and reported elsewhere 3.

Despite the high sensitivity of the methodologies we used, our study is somewhat limited by the sample type FTA cards , which precludes the detection of pathogens in lymphoid tissues and other organs. The use of FTA cards might also introduce biases in the testing protocols, which can favor or hinder the detection of certain types of viruses Both metagenomic protocols conclusively identified Pasteurella spp.

Further analysis of P. Our de novo assembly approach also identified short contigs that could not be attributed to any sequence present in several BLAST databases; of those, only 47 were identified using tblastx. Overall, the amount of unexplained sequence seems relatively small, in particular when considering the substantial number of species of bacterial, viral, and eukaryotic genome that remain either to be discovered or characterized.

The simple fact that not all organisms have been sequenced or are available on central sequence repositories will always contribute to a percentage of unidentifiable reads. The potential pathogenicity is inherent in the organism and can be triggered opportunistically at any time in response to environmental triggers. The epidemiology of and observations on the spatiotemporal distribution of ill animals and carcasses in this study suggests that transmission of bacteria from animal to animal did not occur in most cases except from mothers to calves, which occurred through infected milk.

The near synchronous events in discrete subpopulations, with large distances between aggregations of many hundreds of kilometers, further precluded an infective process spreading across the population. In this situation. Currently user has to create tool which either knows the dependencies in-built or able to scan requirements. Solution: Would be good to have a --recursive flag to the dependency build command in helm.

This would scan the dependencies recursively and fixing any missing charts immediately. In this case, installing the chart will install all of its dependencies. In this case, a chart and its dependencies are managed as a collection. When a chart is packaged via helm package all of its hard dependencies are bundled with it Doesn't the second option make the dependencies specification useless. Can't there be on order of execution for dependencies? Is there a way, that I might have overlooked, to still keep 1 chart with a dependency and somehow make sure that the CRDs defined in the dependency are stored before being used?

TF won't validate the helm release CRDs and will work fine. That's actually a pretty good idea. I've never created helm charts myself. For chart developers, it is often easier to manage dependencies in 'Chart. When a new version is detected, the Action will build the chart.

It will also create a git tag and a GitHub release Build is used to reconstruct a chart's dependencies to the state specified in the lock file. This will not re-negotiate dependencies, as 'helm dependency update' does. Helm is an open source project created by DeisLabs and donated to the Cloud Native Computing Foundation, which now maintains it. Helm is a package manager designed specifically for Kubernetes and improves the management of the YAML manifests required to create Kubernetes projects.

At the heart of Helm is the packaging format called charts. Your Helm charts can use these named templates by declaring the library chart as a dependency. Note that library charts do not deploy anything to your Kubernetes cluster. Instead, they are imported by application charts to simplify chart development and maintenance. Now that we have introduced library charts, let's look at how you can create Developers can modify existing charts or create their own to automate dev, test or production processes.

A YAML file listing dependencies for the chart values. Helm charts use Kubernetes resources to define an application. To find out more about their structure and requirements for their creation, refer to How to Create a Helm Chart. Create a directory containing the common chart files and directories chart. Create new Helm Chart. A Helm "Chart" is a collective noun for a set of folders and files. Create a new Helm Chart: helm create mychart Optionally, The chart folder is populated by the archive of "dependencies" of other charts with its own set of yaml files.

Lint a Chart. You discover that your external dependencies were not updated to support the Kubernetes v1. In turn, your Helm charts do not support v1. In Helm, one chart may depend on any number of other charts. These dependencies can be dynamically linked using the dependencies field in Chart. Managing Dependencies with the dependencies field The charts required by the current chart are defined as a list in the dependencies field.

Make a note of the chart name and proceed to the following step. Step 2: Install a Chart with helm install Command. There are multiple ways to use the helm install command for installing helm charts. The most common is using the chart reference given in the NAME section of the helm search output.. For example, using the syntax explained in the section above, to install Jenkins you would type:This new command will run helm dependency update, pulling the sub chart s from the ChartCenter Helm repository and then package the main chart with sub chart s to the.

Boom, a nice and easy way to use the ChartCenter plugin as a tool to package dependency charts from the ChartCenter helm repo. This only works if you have control of the Helm chart. If the dependency is from a public repo, well there is nothing you can really do. I ultimately discovered a project called the Helmsman which solved a whole bunch of issues I had with Helm.

The Helmsman will even provision the namespaces for you as well as running charts in a very specific All components dependencies an umbrella chart contain, are also Helm charts - this makes it in fact a chart of charts. Those dependencies could be defined within the Chart. I'm probing the community for a way to manage the lifecycle of the helm chart out-of-cluster dependencies, e. IAM roles or storage buckets, as one unit. I've used methods where dependencies are created by an iac engine, Terraform, and injected in helm chart values.

First create mychart Helm will look for the dependencies defined and will download the dependencies from the repository. It will download it as compressed files under the charts folder in the root directory. A chart. The dependencies field in Chart. Resolving External Chart Dependencies — For dependencies on external charts, it is best to publish those to a chart repository for example, using Deploying Bitnami applications as Helm Charts is the easiest way to get started with our applications on Kubernetes.

Our application containers are designed to work well together, are extensively documented, and like our other application formats, our containers are continuously updated when new versions are made available. Helm Installing with Helm. Be sure never to embed cert-manager as a sub-chart of other Helm charts; cert-manager manages non-namespaced resources in your cluster and care must be taken to ensure that it is installed exactly once.

Recently, I was working on refactoring helm charts. As the different microservices evolved, the charts became harder to manage. Many of the charts were sharing some common ConfigMaps and Secrets. One of the main goals of the refactoring was to remove any outside dependencies out of application charts.

The Helm Controller is a Kubernetes operator, allowing one to declaratively manage Helm chart releases with Kubernetes manifests. Based on the creation, mutation or removal of a HelmRelease resource in the cluster, Helm actions are performed by the controller. Use werf helm dependency build to update. All Chart Repositories that are used in. The werf helm repo commands can be used to interact with Chart Repositories: Use werf helm repo add to add Chart Repository.

Use werf helm repo index. Chart is a component representing a collection of resources described by an arbitrary Helm Chart. The Chart can be fetched from any source that is accessible to the helm command line. Values in the values. Objects can be transformed arbitrarily by supplying callbacks to ChartOpts Helm to kustomize Deploying Bitnami applications as Helm Charts is the easiest way to get started with our applications on Kubernetes. Helm packages called charts are a set of Kubernetes manifests that include templates plus a set of values for these templates.

Torrent tracker list 2015 october taurus krieg der engel ebook torrents

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Sometimes most of the time for me , it is not easy to verify the links, but from my experience, this page has the best. Kudos to TTL! Hi, Can you give me the torrent site which is having latest programming video tutorial?

Previously, i have used kickass torrent site, but that site is down. Is there a way to add these trackers to every torrent permanently?? Or do I have to update the trackers every time I add a torrent??? Thank u so much for this list. It has definitely brought download speed difference in my torrent client. Your anti-adblocker is annoying.

You can find a different way to profit from your website. Donate buttons are not offensive. Hi Anon, Thanks for your advice we will disable within this month end and add Donate option. The odds all of those trackers will have the exact same torrent is rare.

It either increases your speed or does nothing. These lists that I use, they are great and help out a lot for quicker downloads. However, there is an issue that I experience when copying and pasting the list into the tracker list. After the addtional items added to the list, there are many, many items on the list that clump together. For an example, say we copy all the items from the list we created from the list above. Then we initiate a download through utorrent. The torrent connects and the download begins.

Cool, but now lets add the addtional items to the tracker list of the particular torrent download. Open the the tracker list either with right-button click the torrent, then select Properties or double click the torrent to open the trackerlist. Once the trackerlist is open, then copy the list above February list and then paste it as an addendom to the existing trackerlist. Then click OK to save the list. All is cool, but not really. Now reopen the tracker list and then examine how the list is posted.

Notice there are many, many clusters of tracker lists that had been clumped together. No matter what I did or how the trackerlist was copied into the torrent trackerlist section, reopening the trackerlist displayed clumps of trackerlist together. The rule of thumb is to have all of the items seperated by a space between each tracker. But when coping a list that has spaces between the trackers, the clumps reappear, even though all of the trackers have spaces between them.

Anyone experience other than me? If so, do you know if there is a solution or if there is a process that can be used to force a space between each tracker in the trackerlist? Sorry about the long-winded post, but it has come to a point that it takes a long time to place spaces manually between each tracker in the trackerlist and wanted to know if there is a sure way of having the spaces between the trackerlist when adding trackers to the torrent.

Thanks for your help. PS, after switching to qBittorrent I never have those problems anymore. Voila, it automatically adds these trackers to all new torrents! I used to be able to do something similar in uTorrent but do not remember how to do it anymore. Hope this helps. The maximum download speed obtainable is whatever you pay your ISP provider for. I can max out at 10 megabytes a second. Thank you very much for this list.

Please me know! Thanks in advance. I refreshed the page to see if it was still waiting for moderation and it is gone. Chanceroo you have entered gmkail. So our system detects your comment as spam. Next time make sure you double check it. Thank you so much for this, It really helps my download speed a lot. Please support the people behind this website! Someone reading this comment, can please recommend to me how to have one VPN, and which one is free to use?

Thanks a lot! How can I find the tracker address by myself? We just want to: 1. Select All 2. COPY 3. PASTE why do I have to scroll down, select a place to start copying, scroll down press shift, then select end point, then right click and copy….

I have made torrent with bittorrent. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Skip to primary navigation Skip to main content Tweet. Share Comments Thanks 4or list, very helpful. Thank you so much for your comment, Luke! We have added space between each torrent tracker. Yo VRK, Learn how to read man. Luke asked them to take the spaces OUT. Wazifa To Call Jinn Allow us to assume, we need to fabricate a house then for building a house heaps of things ought to be utilized.

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List of working torrent trackers May. Posted by Nimeshka Srimal Friday, May 1, comments. Update Notice: 22 September Last updated: October Categories :. Tweet Pin It. Related Post:. Anonymous June 2, at PM. Unknown January 8, at AM. Anonymous June 3, at AM. Unknown March 10, at PM. Properbo June 30, at AM. Princess Celestia December 11, at PM. Nimeshka Srimal December 11, at PM.

Unknown June 17, at AM. Anonymous June 23, at AM. Anonymous November 6, at AM. Anonymous July 5, at PM. Jinesh Shah July 8, at PM. Anonymous July 19, at PM. Anonymous July 24, at AM. Anonymous August 16, at AM. Properbo August 16, at PM. Anonymous August 22, at PM. Unknown September 26, at AM. Unknown August 27, at AM.

Unknown September 13, at PM. Anonymous September 27, at PM. Mohibullah Mamun September 28, at PM. Edoardo November 3, at AM. Unknown November 14, at AM. Kings-Torrent November 15, at AM. Anonymous November 22, at AM. Unknown November 30, at AM.

Anonymous December 12, at PM. Unknown January 25, at AM. Admin May 6, at AM. Unknown September 29, at AM. Anonymous December 10, at AM.

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