Practical cross-engine transactions in dual-engine database systems

With the growing DRAM capacity and core count in modern servers, database systems are becoming increasingly multi-engine to feature a heterogeneous set of engines. In particular, a memory-optimized engine and a conventional storage-centric engine may coexist to satisfy various application needs. However, handling cross-engine transactions that access more than one engine remains challenging in terms of correctness, performance and programmability. This thesis describes Skeena, an approach to cross-engine transactions with proper isolation guarantees and low overhead. Skeena adapts and integrates past concurrency control theory to provide a complete solution to supporting various isolation levels in dual-engine systems, and proposes a lightweight transaction tracking structure that captures the necessary information to guarantee correctness with low overhead. Evaluation on a 40-core server shows that Skeena only incurs minuscule overhead for cross-engine transactions, without penalizing single-engine transactions

ICPD-A New Peak Detection Algorithm for LC/MS

BACKGROUND: The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery. RESULTS: In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection. CONCLUSIONS: The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods

Traffic-Aware Transmission Mode Selection in D2D-enabled Cellular Networks with Token System

We consider a D2D-enabled cellular network where user equipments (UEs) owned by rational users are incentivized to form D2D pairs using tokens. They exchange tokens electronically to "buy" and "sell" D2D services. Meanwhile the devices have the ability to choose the transmission mode, i.e. receiving data via cellular links or D2D links. Thus taking the different benefits brought by diverse traffic types as a prior, the UEs can utilize their tokens more efficiently via transmission mode selection. In this paper, the optimal transmission mode selection strategy as well as token collection policy are investigated to maximize the long-term utility in the dynamic network environment. The optimal policy is proved to be a threshold strategy, and the thresholds have a monotonicity property. Numerical simulations verify our observations and the gain from transmission mode selection is observed.Comment: 7 pages, 6 figures. A shorter version is submitted to EUSIPC

What Are Lacking in Sora and V-JEPA’s World Models? -A Philosophical Analysis of Video AIs Through the Theory of Productive Imagination

Sora from Open AI has shown exceptional performance, yet it faces scrutiny over whether its technological prowess equates to an authentic comprehension of reality. Critics contend that it lacks a foundational grasp of the world, a deficiency V-JEPA from Meta aims to amend with its joint embedding approach. This debate is vital for steering the future direction of Artificial General Intelligence(AGI). We enrich this debate by developing a theory of productive imagination that generates a coherent world model based on Kantian philosophy. We identify three indispensable components in the coherent world model that enable genuine world understanding: latent representations of isolated objects, an a priori law of change across space and time, and Kantian categories. Our analysis reveals that Sora is limited because of its oversight of the a priori law of change and Kantian categories, flaws that are not rectifiable through scaling up the training. V-JEPA learns the context-dependent aspect of the a priori law of change. Yet it fails to fully comprehend Kantian categories and incorporate experience, leading us to conclude that neither system currently achieves a comprehensive world understanding. Nevertheless, each system has developed components essential to advancing an integrated AI productive imagination-understanding engine. Finally, we propose an innovative training framework for an AI productive imagination-understanding engine, centered around a joint embedding system designed to transform disordered perceptual input into a structured, coherent world model. Our philosophical analysis pinpoints critical challenges within contemporary video AI technologies and a pathway toward achieving an AI system capable of genuine world understanding, such that it can be applied for reasoning and planning in the future

MRCQuant- an accurate LC-MS relative isotopic quantification algorithm on TOF instruments

<p>Abstract</p> <p>Background</p> <p>Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-of-flight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository.</p> <p>Results</p> <p>MRCQuant showed significant improvement in the number of accurately quantified peptides.</p> <p>Conclusions</p> <p>MRCQuant effectively addresses major issues in the relative quantification of LC-MS-based proteomics data, and it provides improved performance in the quantification of low abundance peptides.</p

SCFIA: a statistical corresponding feature identification algorithm for LC/MS

<p>Abstract</p> <p>Background</p> <p>Identifying corresponding features (LC peaks registered by identical peptides) in multiple Liquid Chromatography/Mass Spectrometry (LC-MS) datasets plays a crucial role in the analysis of complex peptide or protein mixtures. Warping functions are commonly used to correct the mean of elution time shifts among LC-MS datasets, which cannot resolve the ambiguity of corresponding feature identification since elution time shifts are random. We propose a Statistical Corresponding Feature Identification Algorithm(SCFIA) based on both elution time shifts and peak shape correlations between corresponding features. SCFIA first trains a set of statistical models, and then, all candidate corresponding features are scored by the statistical models to find the maximum likelihood solution.</p> <p>Results</p> <p>We test SCFIA on publicly available datasets. We first compare its performance with that of warping function based methods, and the results show significant improvements. The performance of SCFIA on replicates datasets and fractionated datasets is also evaluated. In both cases, the accuracy is above 90%, which is near optimal. Finally the coverage of SCFIA is evaluated, and it is shown that SCFIA can find corresponding features in multiple datasets for over 90% peptides identified by Tandem MS.</p> <p>Conclusions</p> <p>SCFIA can be used for accurate corresponding feature identification in LC-MS. We have shown that peak shape correlation can be used effectively for improving the accuracy. SCFIA provides high coverage in corresponding feature identification in multiple datasets, which serves the basis for integrating multiple LC-MS measurements for accurate peptide quantification.</p

Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks

<p>Abstract</p> <p>Background</p> <p>Transcriptional regulation by transcription factor (TF) controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task.</p> <p>Results</p> <p>We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method.</p> <p>Conclusions</p> <p>The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data.</p

Review of Peak Detection Algorithms in Liquid-Chromatography-Mass Spectrometry

In this review, we will discuss peak detection in Liquid-Chromatography-Mass Spectrometry (LC/MS) from a signal processing perspective. A brief introduction to LC/MS is followed by a description of the major processing steps in LC/MS. Specifically, the problem of peak detection is formulated and various peak detection algorithms are described and compared

Histone Deacetylase 3-Directed PROTACs Have Anti-inflammatory Potential by Blocking Polarization of M0-like into M1-like Macrophages

Macrophage polarization plays a crucial role in inflammatory processes. The histone deacetylase 3 (HDAC3) has deacetylase-independent function that can activate pro-inflammatory gene expression in LPS-stimulated M1-like macrophages and cannot be blocked by traditional small-molecule HDAC3 inhibitors. Here we employ the proteolysis targeting chimera (PROTAC) technology to target the deacetylase-independent function of HDAC3. We developed a potent and selective HDAC3-directed PROTAC, denoted P7, which induces nearly complete HDAC3 degradation at low micromolar concentrations in both THP-1 cells and human primary macrophages. P7 increases the anti-inflammatory cytokine secretion in THP-1 derived M1-like macrophages. Importantly, P7 decreases the secretion of pro-inflammatory cytokines in M1-like macrophages derived from human primary macrophages. This can be explained by the observed inhibition of macrophage polarization from M0-like into M1-like macrophage. In conclusion, we demonstrate that the HDAC3-directed PROTAC P7 has anti-inflammatory activity and blocks macrophage polarization, which demonstrates that this molecular mechanism can be targeted with small molecule therapeutics.</p