Re: Tolerance Data 2012 Torrent
Jemima Torguson
During external beam radiotherapy, normal tissues are irradiated along with the tumor. Radiation therapists try to minimize the dose of normal tissues while delivering a high dose to the target volume. Often this is difficult and complications arise due to irradiation of normal tissues. These complications depend not only on the dose but also on volume of the organ irradiated. Lyman has suggested a four-parameter empirical model which can be used to represent normal tissue response under conditions of uniform irradiation to whole and partial volumes as a function of the dose and volume irradiated. In this paper, Lyman's model has been applied to a compilation of clinical tolerance data developed by Emami et al. The four parameters to characterize the tissue response have been determined and graphical representations of the derived probability distributions are presented. The model may, therefore, be used to interpolate clinical data to provide estimated normal tissue complication probabilities for any combination of dose and irradiated volume for the normal tissues and end points considered.
Objective: The glucose tolerance test (GTT) is widely used in human and animal biomedical and pharmaceutical research. Despite its prevalent use, particularly in mouse metabolic phenotyping, to the best of our knowledge we are not aware of any studies that have attempted to qualitatively compare the metabolic events during a GTT in mice with those performed in humans.
Methods: Stable isotope labelled oral glucose tolerance tests (siOGTTs; [6,6-2H2]glucose) were performed in both human and mouse cohorts to provide greater resolution into postprandial glucose kinetics. The siOGTT allows for the partitioning of circulating glucose into that derived from exogenous and endogenous sources. Young adults spanning the spectrum of normal glucose tolerance (n = 221), impaired fasting (n = 14), and impaired glucose tolerance (n = 19) underwent a 75g siOGTT, whereas a 50 mg siOGTT was performed on chow (n = 43) and high-fat high-sucrose fed C57Bl6 male mice (n = 46).
Results: During the siOGTT in humans, there is a long period (>3hr) of glucose absorption and, accordingly, a large, sustained insulin response and robust suppression of lipolysis and endogenous glucose production (EGP), even in the presence of glucose intolerance. In contrast, mice appear to be highly reliant on glucose effectiveness to clear exogenous glucose and experience only modest, transient insulin responses with little, if any, suppression of EGP. In addition to the impaired stimulation of glucose uptake, mice with the worst glucose tolerance appear to have a paradoxical and persistent rise in EGP during the OGTT, likely related to handling stress.
Conclusions: The metabolic response to the OGTT in mice and humans is highly divergent. The potential reasons for these differences and their impact on the interpretation of mouse glucose tolerance data and their translation to humans are discussed.
Artificially synthesized short interfering RNAs (siRNAs) are widely used in functional genomics to knock down specific target genes. One ongoing challenge is to guarantee that the siRNA does not elicit off-target effects. Initial reports suggested that siRNAs were highly sequence-specific; however, subsequent data indicates that this is not necessarily the case. It is still uncertain what level of similarity and other rules are required for an off-target effect to be observed, and scoring schemes have not been developed to look beyond simple measures such as the number of mismatches or the number of consecutive matching bases present. We created design rules for predicting the likelihood of a non-specific effect and present a web server that allows the user to check the specificity of a given siRNA in a flexible manner using a combination of methods. The server finds potential off-target matches in the corresponding RefSeq database and ranks them according to a scoring system based on experimental studies of specificity.
Data replication is the process of creating multiple copies of the same data.There is no data replication as you see in other systems like Kafka, Pinot etc since Spark is a data processing engine instead of a data store. That being said, when a data is read, its split into smaller units and stored in each node and further transformations are applied on this. Hence the term distributed.
How spark achieves fault tolerance is through lineage graphs. These graphs keeps track of transformations to be executed on an RDD after an action is called. Lineage Graph helps recompute damaged RDDs.
- In choosing between the RM57 and TMS570 architectures, beside the temperature rating differences, what internal process variation exists to inform a decision as to which product line is better aligned for a radiation environment? Ideally, I'd like to stick to the RM57 since those are little-endian processors which would make software development and third-party code integration a bit easier; that being said, if there's a compelling reason to go for the TMS570 lineup for SEL tolerance reasons I would like to get some data for this.
- For any of the Hercules line of processors (focusing on RM57/TMS570), does TI have TID/SEL data that can be made available, possibly under NDA? If so, who is the point of contact to get this process started?
for the TMS570LC4357-EP device from TI's site, and it appears there is at least high-level radiation (both LET and TID) test data on that document. I was wondering if a more detailed report (at least providing some visibility into test conditions, types of radiation utilized and package degradation data) is available offline.
As per TI quality guidelines, radiation test data cannot be provided for any part not qualified for use in space applications. These parts have a special designation ("SEP" for Space EP) that indicates suitability for use in Space applications. We can only provide radiation test data for SEP parts.
If you'd like to discuss the testing details offline, feel free to PM me. Would be happy to provide further details on what I'm looking for. I was going off the post made by another user on this forum (I can share the link offline if you want a reference), and assuming this data can be made available if a waiver/NDA is processed. Thanks.
The analog input resistance of the ADC is 100ohm differential and is part of the IC process, these resistors can have a wider range of tolerance, even at ambient temperature. See the S11 plot in figure 39.
If you plan to design an anti-aliasing filter with an amplifier, I would use the 200ohm differential load specified in the datasheet for the filter design. I would also leave an option to back terminate to help "stiffen" the impedance for what is actually needed.
I'm writing a big batch job using PySpark that ETLs 200 tables and loads into Amazon Redshift.These 200 tables are created from one input datasource. So the batch job is successful only when data is loaded into ALL 200 tables successfully. The batch job runs everyday while appending the data into tables for each date.
This way I can guarantee that if Step 3 fails (which is more probable), I don't have to worry about removing partial data from original tables. Rather, I'll simply re-run entire batch job since temporary tables are discarded after the JDBC disconnection.
I am creating an Azure Data factory to copy binary files from the Google Cloud Storage bucket to the Azure Blob Storage container. The files need to be copied without any compression.I want to specify fault tolerance settings to skip files with invalid names and skip forbidden files but those options are disabled when I create the copy pipeline using Azure portal.
The phrase fault tolerant is often used to describe data centers. Seen as a standard of quality and a sure sign of reliability, a fault tolerant data center is one that has no single point of failure. Facilities are purpose-built to avoid such a point of failure and fully equipped with a range of technology that significantly improves the fault tolerance of the center as a whole.
Tier I data centers are amongst the most affordable options. While they do not provide the high levels of fault tolerance that Tier IV centers will, they are usually sufficient for the needs of companies looking for a basic level of support for existing systems. These data centers tend to include features like cooling equipment, engine generators, and an uninterruptible power supply.
The basic level of service that Tier I data centers provide is improved by those in the Tier II bracket. These data centers also include power and cooling components, which help companies to complete maintenance tasks without disrupting systems. Such components are also useful in limiting the chance of any downtime caused by equipment failures.
Tier III data centers provide a clear benefit to companies that are always looking to expand and improve the service they offer. They are built in such a way that shutdowns are never required during maintenance tasks, and equipment can be replaced with no need for any downtime at all. This is achieved through the addition of a redundant delivery path, which is used for power and cooling, alongside all the redundant critical components of a Tier II data center.
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