Packed Memory Arrays – Rewired

The physical memory layout of a tree-based index structure deteriorates over time as it sustains more updates; such that sequential scans on the physical level become non-sequential, and therefore slower. Packed Memory Arrays (PMAs) prevent this by managing all data in a sequential sparse array. PMAs have been studied mostly theoretically but suffer from practical problems, as we show in this paper. We study and fix these problems, resulting in an improved data structure: the Rewired Memory Array (RMA). We compare RMA with the main previous PMA implementations as well as state-of-the-art tree index structures and show on a wide variety of data and query distributions that RMA can reach competitive update and point lookup performance, while always providing superior scan performance – close to dense column scans

Online List Labeling with Predictions

A growing line of work shows how learned predictions can be used to break through worst-case barriers to improve the running time of an algorithm. However, incorporating predictions into data structures with strong theoretical guarantees remains underdeveloped. This paper takes a step in this direction by showing that predictions can be leveraged in the fundamental online list labeling problem. In the problem, n items arrive over time and must be stored in sorted order in an array of size Theta(n). The array slot of an element is its label and the goal is to maintain sorted order while minimizing the total number of elements moved (i.e., relabeled). We design a new list labeling data structure and bound its performance in two models. In the worst-case learning-augmented model, we give guarantees in terms of the error in the predictions. Our data structure provides strong guarantees: it is optimal for any prediction error and guarantees the best-known worst-case bound even when the predictions are entirely erroneous. We also consider a stochastic error model and bound the performance in terms of the expectation and variance of the error. Finally, the theoretical results are demonstrated empirically. In particular, we show that our data structure has strong performance on real temporal data sets where predictions are constructed from elements that arrived in the past, as is typically done in a practical use case

Simulation modelling and visualisation: toolkits for building artificial worlds

Simulations users at all levels make heavy use of compute resources to drive computational simulations for greatly varying applications areas of research using different simulation paradigms. Simulations are implemented in many software forms, ranging from highly standardised and general models that run in proprietary software packages to ad hoc hand-crafted simulations codes for very specific applications. Visualisation of the workings or results of a simulation is another highly valuable capability for simulation developers and practitioners. There are many different software libraries and methods available for creating a visualisation layer for simulations, and it is often a difficult and time-consuming process to assemble a toolkit of these libraries and other resources that best suits a particular simulation model. We present here a break-down of the main simulation paradigms, and discuss differing toolkits and approaches that different researchers have taken to tackle coupled simulation and visualisation in each paradigm

Cache-Oblivious Representation of B-Tree Structures

We present a data structure CORoBTS for storing a search tree with all leaves at the same depth and vertices of arity between chosen constants $a$ and $b$ in a cache-oblivious way. It provides operations for inserting an $a$-ary subtree and removing a subtree; both have an amortized I/O complexity $\mathcal{O}(S\cdot(\log^2 N)/ B + \log_B N \cdot \log\log S + 1)$ and amortized time complexity $\mathcal{O}(S\cdot\log^2 N)$, where $S$ is the size of the subtree and $N$ size of the whole stored tree. The tree allows searching with an optimal I/O complexity $\mathcal{O}(\log_B{N})$ and is stored in a linear space. We use the data structure as a top space-time tree in the cache-oblivious partially persistent array proposed by Davoodi et al. [DFI\"O14]. The space complexity of the persistent array is then improved from $\mathcal{O}(U^{\log_2 3} + V \log U)$ to $\mathcal{O}(U + V \log U)$, where $U$ is the maximal size of the array and $V$ is the number of versions. The data locality and I/O complexity of both present and persistent reads are kept unchanged; I/O complexity of writes is worsened by a polylogarithmic factor.Comment: 26 pages + 7 pages of algorithms, 7 figure

Molecular characterization of the synapse from a proteomic perspective

The synapse is the most characteristic feature of the brain that allows the flow of information encoding our cognitive functions, behavior and memory. Slight perturbations in synaptic function can derive in wide range of psychiatric, neurodevelopmental and neurodegenerative disorders. The aim of this thesis was to investigate the synaptic proteome and interactome in order to gain insights in the molecular mechanisms underlying synaptic function. To this end, we exploited the potential of multiple advanced mass spectrometry methodologies for protein identification, quantification, and protein interaction determination. In chapter 2, I investigated the molecular development of the synapse. This process requires prominent changes of the synaptic proteome and potentially involves thousands of different proteins at every synapse. We analyzed the cortical synaptic membrane proteome of juvenile, adolescent and adult mice brains using iTRAQ-based DDA quantitative proteomics. In several cases, proteins from a single functional molecular entity, e.g., subunits of the NMDA receptor, showed differences in their temporal regulation, which may reflect specific synaptic development features of connectivity, strength and plasticity. We also evaluated the function of Cxadr, a protein with high expression level at early stages and a fast decline in expression during neuronal development. Knockdown of the expression of Cxadr in cultured primary mouse neurons revealed a significant decrease in synapse density. Altogether, these results reveal the expression profile of synaptic proteome during development and provide new insights into the molecular processes underlying synaptogenesis and synapse maturation. In chapter 3, I explored the mechanism behind the synaptic modulation mediated by the metabotropic glutamate receptor 5. mGluR5 plays a major role in the modulation of synaptic function and plasticity, as well as in several brain disorders. Despite robust pre-clinical data, mGluR5 antagonists failed in several clinical trials, highlighting the need for a better understanding of the mechanisms underlying mGluR5 function. Using a proteomic approach, we determined the molecular response of the synapse to a reduction of mGluR5 activity by pharmacological inhibition and gene deletion. In both cases, the most prominent response of the synaptic proteome was the change in protein expression of key mitochondrial pathways. Together with this, we observed morphological and functional alterations of mitochondria in mGluR5 KO synapses. Our findings provide new insight into a functional connection of mGluR5 and specific mitochondrial function. In chapter 4, I applied XL-MS as entry into the synapse interactome, in particular to reveal the architecture and assembly of synaptic protein complexes. As a result, we generated to the first large-scale cross-linking repository in the brain. The reliability of the data was validated by several approaches as we deemed necessary for a recent methodology. In addition, a large part of the crosslink data contains novel information which allowed us to identify novel protein partners, to model protein conformational dynamics, and to delineate within and between protein interactions of main synaptic constituents, such as Camk2, the AMPA-type glutamate receptor, and associated proteins. Given the molecular complexity of the synapse and the large amount and depth of the data generated, we provided the complete dataset as an interactive web-based platform for further investigations (http://xlink.cncr.nl). Together, we generated one of the largest cross-linking collections that provided new entries into exploration of protein structures and interactions. Collectively, the application and development of multiple proteomic methodologies allowed us to reveal several aspects of the molecular architecture of the synapse, including protein composition, function, structure and interaction. Beyond the new insights uncovered for specific proteins in this thesis, the data resources generated can be further used for probing additional proteins and contributes to improve our understanding of synapse function and brain disease

Acute Response of Peripheral Blood Cell to Autologous Hematopoietic Stem Cell Transplantation in Type 1 Diabetic Patient

Autologous nonmyeloablative hematopoietic stem cell transplantation (AHST) was the first therapeutic approach that can improve β cell function in type 1 diabetic (T1D) patients. This study was designed to investigate the potential mechanisms involved.We applied AHST to nine T1D patients diagnosed within six months and analyzed the acute responses in peripheral blood for lymphocyte subpopulation as well as for genomic expression profiling at the six-month follow-up.We found six patients obtained insulin free (IF group) and three remained insulin dependent (ID group); C-peptide production was significantly higher in IF group compared to ID group. The acute responses in lymphocytes at six-month follow-up include declined CD3(+)CD4(+), CD3(+)CD8(+) T cell population and recovered B cell, NK cell population in both groups but with no significant differences between the two groups; most immune-related genes and pathways were up-regulated in peripheral blood mononuclear cell (PBMC) of both groups while none of transcription factors for immune regulatory component were significantly changed; the IF group demonstrated more AHST-modified genetic events than the ID group and distinct pattern of top pathways, co-expression network as well as 'hub' genes (eg, TCF7 and GZMA) were associated with each group.AHST could improve the islet function in newly diagnosed T1D patients and elimination of the islet specific autoreactive T cells might be one of the mechanisms involved; T1D patients responded differently to AHST possibly due to the distinct transcriptional events occurring in PBMC.ClinicalTrials.gov NCT00807651

Visual Programming: Concepts and Implementations

The computing environment has changed dramatically since the advent of the computer. Enhanced computer graphics and sheer processing power have ushered in a new age of computing. User interfaces have advanced from simple line entry to powerful graphical interfaces. With these advances, computer languages are no longer forced to be sequentially and textually-based. A new programming paradigm has evolved to harness the power of today's computing environment - visual programming. Visual programming provides the user with visible models which reflect physical objects. By connecting these visible models to each other, an executable program is created. By removing the inherent abstractions of textual languages, visual programming could lead computing into a new era

Taking the Shortcut: Actively Incorporating the Virtual Memory Index of the OS to Hardware-Accelerate Database Indexing

Index structures often materialize one or multiple levels of explicit indirections (aka pointers) to allow for a quick traversal to the data of interest. Unfortunately, dereferencing a pointer to go from one level to the other is costly since additionally to following the address, it involves two address translations from virtual memory to physical memory under the hood. In the worst case, such an address translation is resolved by an index access itself, namely by a lookup into the page table, a central hardware-accelerated index structure of the OS. However, if the page table is anyways constantly queried, it raises the question whether we can actively incorporate it into our database indexes and make it work for us. Precisely, instead of materializing indirections in form of pointers, we propose to express these indirections directly in the page table wherever possible. By introducing such shortcuts, we (a) effectively reduce the height of traversal during lookups and (b) exploit the hardware-acceleration of lookups in the page table. In this work, we analyze the strengths and considerations of this approach and showcase its effectiveness at the case of the real-world indexing scheme extendible hashing