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INTRODUCTION: Brain glucose hypometabolism, indexed by the fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) imaging, is a metabolic signature of Alzheimer's disease (AD). However, the underlying biological pathways involved in these metabolic changes remain elusive. METHODS: Here, we integrated [18F]FDG-PET images with blood and hippocampal transcriptomic data from cognitively unimpaired (CU, n = 445) and cognitively impaired (CI, n = 749) individuals using modular dimension reduction techniques and voxel-wise linear regression analysis. RESULTS: Our results showed that multiple transcriptomic modules are associated with brain [18F]FDG-PET metabolism, with the top hits being a protein serine/threonine kinase activity gene cluster (peak-t(223) = 4.86, P value < 0.001) and zinc-finger-related regulatory units (peak-t(223) = 3.90, P value < 0.001). DISCUSSION: By integrating transcriptomics with PET imaging data, we identified that serine/threonine kinase activity-associated genes and zinc-finger-related regulatory units are highly associated with brain metabolic changes in AD. HIGHLIGHTS: We conducted an integrated analysis of system-based transcriptomics and fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) at the voxel level in Alzheimer's disease (AD). The biological process of serine/threonine kinase activity was the most associated with [18F]FDG-PET in the AD brain. Serine/threonine kinase activity alterations are associated with brain vulnerable regions in AD [18F]FDG-PET. Zinc-finger transcription factor targets were associated with AD brain [18F]FDG-PET metabolism.
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The recent research effort aiming to provide a royalty-free video format resulted in AOMedia Video 1 (AV1), which was launched in 2018. AV1 was developed by the Alliance for Open Media (AOMedia), which groups several major technology companies such as Google, Netflix, Apple, Samsung, Intel, and many others. AV1 is currently one of the most prominent video formats and has introduced several complex coding tools and partitioning structures in comparison to its predecessors. A study of the computational effort required by the different AV1 coding steps and partition structures is essential for understanding its complexity distribution when implementing fast and efficient codecs compatible with this format. Thus, this paper presents two main contributions: first, a profiling analysis aiming at understanding the computational effort required by each individual coding step of AV1; and second, a computational cost and coding efficiency analysis related to the AV1 superblock partitioning process. Experimental results show that the two most complex coding steps of the libaom reference software implementation are the inter-frame prediction and transform, which represent 76.98% and 20.57% of the total encoding time, respectively. Also, the experiments show that disabling ternary and asymmetric quaternary partitions provide the best relationship between coding efficiency and computational cost, increasing the bitrate by only 0.25% and 0.22%, respectively. Disabling all rectangular partitions provides an average time reduction of about 35%. The analyses presented in this paper provide insightful recommendations for the development of fast and efficient AV1-compatible codecs with a methodology that can be easily replicated.
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BACKGROUND: Changes in soluble amyloid-beta (Aß) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer's disease (AD). However, whether Aß levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aß isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. METHODS: We used CSF measurements of three soluble Aß peptides (Aß1-38, Aß1-40 and Aß1-42) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals. RESULTS: Our findings indicate that Aß isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML model. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed. CONCLUSIONS: Our results demonstrate that, by applying a refined ML analysis, a combination of Aß isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction.
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The digital video coding process imposes severe pressure on memory traffic, leading to considerable power consumption related to frequent DRAM accesses. External off-chip memory demand needs to be minimized by clever architecture/algorithm co-design, thus saving energy and extending battery lifetime during video encoding. To exploit temporal redundancies among neighboring frames, the motion estimation (ME) algorithm searches for good matching between the current block and blocks within reference frames stored in external memory. To save energy during ME, this work performs memory accesses distribution analysis of the test zone search (TZS) ME algorithm and, based on this analysis, proposes both a multi-sector scratchpad memory design and dynamic management for the TZS memory access. Our dynamic memory management, called neighbor management, reduces both static consumption-by employing sector-level power gating-and dynamic consumption-by reducing the number of accesses for ME execution. Additionally, our dynamic management was integrated with two previously proposed solutions: a hardware reference frame compressor and the Level C data reuse scheme (using a scratchpad memory). This system achieves a memory energy consumption savings of 99.8 % and, when compared to the baseline solution composed of a reference frame compressor and data reuse scheme, the memory energy consumption was reduced by 44.1 % at a cost of just 0.35 % loss in coding efficiency, on average. When compared with related works, our system presents better memory bandwidth/energy savings and coding efficiency results.