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New user interface and intuitive Radeon Software Installer that includes options for express install, custom install and clean uninstall. The new installer will also show options for the latest available driver for your system configuration during the install process.
Secure Boot is based on the Public Key Infrastructure (PKI) process to authenticate modules before they are allowed to execute. These modules can include firmware drivers, option ROMs, UEFI drivers on disk, UEFI applications, or UEFI boot loaders. Through image authentication before execution, Secure Boot reduces the risk of pre-boot malware attacks such as rootkits. Microsoft relies on UEFI Secure Boot in Windows 8 and above as part of its Trusted Boot security architecture to improve platform security for our customers. Secure Boot is required for Windows 8 and above client PCs, and for Windows Server 2016 as defined in the Windows Hardware Compatibility Requirements.
On non-Windows RT PCs the OEM should consider including the Microsoft Corporation UEFI CA 2011 with a SHA-1 Certificate Hash of 46 de f6 3b 5c e6 1c f8 ba 0d e2 e6 63 9c 10 19 d0 ed 14 f3. Signing UEFI drivers and applications with this certificate will allow UEFI drivers and applications from 3rd parties to run on the PC without requiring additional steps for the user. The UEFI CA can be downloaded from here: =321194.
On non-Windows RT PCs the OEM may also have additional items in the db to allow other operating systems or OEM-approved UEFI drivers or apps, but these images must not compromise the security of the PC in any way.
These work great with standalone servers. One can use Microsoft CAPI and CNG or any other secure API supported by HSM. These HSMs come in variety of form factors supporting USB, PCIe and PCMCIA buses.
UEFI Drivers must be signed by a CA or key in the db as described elsewhere in the document, or have the hash of the driver image included in db. Microsoft will be providing a UEFI driver signing service similar to the WHQL driver signing service using the Microsoft Corporation UEFI CA 2011. Any drivers signed by this will run seamlessly on any PCs that include the Microsoft UEFI CA. It is also possible for an OEM to sign trusted drivers and include the OEM CA in the db, or to include hashes of the drivers in the db. In all cases a UEFI driver (Option ROM) shall not execute if it is not trusted in the db.
Microsoft has this made available to anyone who wants to sign UEFI drivers. This certificate is part of the Windows HCK Secure Boot tests. Follow [this blog](( _hardware_certification/2013/12/03/microsoft-uefi-ca-signing-policy-updates/) to read more about UEFI CA signing policy and updates.
Implications of FN results can be significant, potentially leading to positive case clusters and negative outcomes . Current guidance from the World Health Organization (WHO) and others calls for repeat testing (including sampling of the lower respiratory tract) in individuals who continue to display symptoms of COVID-19 with continued infection prevention measures [9, 11, 12]. The optimal interval of repeat testing is not clear with different studies suggesting a range from 1 to 6 days following the first negative test [13, 14].
Changes in microbial compositions following FMT have been studied with regard to phages22 or fungi23,24, yet the bulk of current knowledge is focused on bacteria and archaea where colonization by donor microbes and the persistence of indigenous recipient microbes emerge at the strain level of microbial populations25. Strain-level studies suggest that colonization levels following FMT vary across indications: whereas donor and recipient strains coexist long term in metabolic syndrome (MetS) patients25, donor takeover is the most common outcome in rCDI26,27,28, with intermediate outcomes in UC29 or obesity30,31. However, the factors shaping these differential strain-level outcomes remain poorly understood. In small pilot study cohorts, colonization success of donor strains leading to short-term persistence was associated with species phylogeny, broad microbial phenotypes and relative fecal abundances in rCDI26,27, but with more adaptive metabolic phenotypes in UC32.
We built LASSO models that were restricted to different subcategories of predictor variables and compared their performance with full models trained on the entire complements of ex ante or post hoc variables (Fig. 5a). Models trained exclusively on recipient pre-FMT species abundances, on abundance and strain population characteristics of the focal species and, to a lesser degree, on microbiome community diversity variables achieved highest accuracies, comparable to those of full models. Notably, predictive power of individual recipient species was due almost entirely to exclusion effects, meaning that the enrichment of certain species in the recipient was associated with less donor takeover or recipient strain turnover of others, while facilitation effects did not have a contributing role. Models restricted to procedural factors (including disease indication), pre-FMT metabolic state or donor species abundances achieved much lower accuracies than full models, indicating that these variable groups were less predictive of strain-level outcomes. Overall, we observed similar trends for models trained on post hoc variables (Fig. 5a, right).
We observed few prominent predictive species in the donor microbiota, most notably B. vulgatus and Evtepia gabavorous. Facilitation and inhibition effects of donor species were generally limited and overall less predictive of colonization success, indicating that the donor microbiota has limited impact on colonization outcome beyond intraspecific strain dynamics.
Recipient factors consistently outweighed donor factors in driving FMT strain-level outcomes. Thus, our data did not support the super-donor hypothesis15 which states that certain donor microbiome properties are crucial to colonization and, by proxy, clinical success. Rather, we found that complementarity of donor and recipient microbiomes promoted donor colonization and recipient turnover. This phenomenon was observed across microbial resolutions, from community-level effects to conspecific strain population dissimilarity. Indeed, strain-level diversity and complementarity were the strongest determinants of FMT outcome, with relevance to rational donor selection in clinical practice16,35. Beyond screening for donor health, matching of donors to recipients based on microbiome complementarity at community, species and, in particular, strain levels may increase colonization success, make clinical outcomes more predictable and reduce adverse effects.
Our results indicate that microbiome dynamics following FMT are impacted by defined parameters that are tunable in clinical practice, thus supporting the notion that predictable and efficacious microbiome modulation using personalized probiotic mixtures, rather than entire complex fecal samples, is possible and may profit from an ecological perspective. In particular, our findings suggest that the targeted depletion of selected microbes in the recipient, with concurrent introduction of diverse strain populations of the same species rather than a single strain, presents a promising approach to enhancing colonization and turnover in the recipient, although links to clinical outcomes remain to be established. Thus, levering of both neutral and relevant adaptive ecological processes may pave the way towards targeted modulatory interventions on the gut microbiome, personalized to patients, with predictable microbiome-level outcomes.
We explored a large set of covariates as putative predictor variables for FMT outcomes, grouped into the following categories: (1) host clinical and procedural variables (for example, FMT indication, pre-FMT bowel preparation, FMT route and so on); (2) community-level taxonomic diversity (species richness, community composition and so on); (3) community-level metabolic profiles (abundance of specific pathways); (4) abundance profiles of individual species; (5) strain-level outcomes for other species in the system; and (6) focal species characteristics, including strain-level diversity; see Supplementary Table 6 for a full list of covariates and their definitions. We further classified covariates as either predictive ex ante variables (that is, knowable before the FMT is conducted) or post hoc variables (that is, pertaining to the post-FMT state, or the relation between pre- and post-FMT states). 2b1af7f3a8