539 research outputs found
Performance analysis of the WiNC2R platform:
No full textA Cognitive Radio (CR) is an intelligent transceiver device, able to support multiple technologies, dynamic re-configurability, ease of programming and collaboration with other CR devices to improve the communication efficiency. The two key requirements for an efficient CR implementation are flexibility in operation/programming and speed.
WiNC2R (Winlab Network Centric Cognitive Radio) achieves high speed of operation using its hardware platform and flexibility using its software-configurable architecture. The current WiNC2R architecture implements an 802.11a-like OFDM flow. We evaluate the WiNC2R hardware architecture to see the modularity in the architecture, separation of data and control flow and the performance in terms of latency and throughput. To test the system, the Xilinx Bus Functional Model environment, which is designed to test the IBM standard bus-architecture-based hardware systems, is used. We use a simple ALOHA protocol in the MAC layer to communicate between two WiNC2R nodes and evaluate the performance under the best-case scenario, where the performance is only hindered by the architecture itself rather than external conditions like channel state.
The results of our basic experiments showed that for a single OFDM 802.11a-like flow, the Unit Control Modules (UCM) were idle for almost 80% of the total processing time.
We then tested the WiNC2R system to study the effects of changing the frame size. It was seen that the latencies in the WiNC2R transmitter are frame-size dependent while those in the receiver mainly depend on the size of the data in the last chunk rather than the size of the whole frame. We suggest that chunk size should be 2 OFDM symbols, and chunking be moved to MAC layer for better performance. We give analytical estimates of resulting performance improvement. In the next experiment, we describe virtualization in the WiNC2R by adding more flows. We describe the steps to implement the additional flows and estimate maximum number of concurrent flows possible.
In the last analysis, we show the effect of operating clock frequency on the performance. We prove that at 250 MHz operating frequency and 2 OFDM symbols per chunk, the current WiNC2R implementation will be able to satisfy the SIFS criterion.M.S.Includes bibliographical references (p. 72-73)by Sumit Satarka
Online Communities Support Policy-Making: The Need for Data Analysis
No full textKlinger R, Senger P, Madan S, Jacovi M. Online Communities Support Policy-Making: The Need for Data Analysis. In: Tambouris E, Macintosh A, Sæbø Ø, eds. Electronic Participation. Lecture Notes in Computer Science. Vol 7444. Springer Berlin Heidelberg; 2012: 132-143
Personalized drug recommendation with pretrained GNNs on a large biomedical knowledge graph
No full textPrescribing drugs to patients is usually performed based on their condition and the expertise of the medical practitioners. However, finding the most suitable drug is a complex task, as doctors might be influenced not only by the patient’s characteristics but also by factors like drug availability, insurance coverage, and even personal preference. With an increase in data availability and quality from vast knowledge bases focusing on individual traits and biological interactions, computational drug recommendation systems emerged. These tools hold the potential to improve the decision-making for drug prescriptions of a single doctor. In this work, we adapted the graph in-context learning framework Prodigy proposed by Huang et al. to perform personalized drug prediction for individual patients. First, patient-specific knowledge graphs were created using EHR data of patients from UK Biobank and the biomedical knowledge graph PrimeKG. Based on unique patient profiles, incorporating drug-, disease-, genomic-, and demographic information, disease-specific drugs are recommended by using patients with the same disease and known prescriptions as support examples. The performance of our proposed model was evaluated for three psychiatric disorders, namely depression, bipolar disorder, and schizophrenia, by assessing how well the drugs connected with the disease are considered as true over false drugs regarding treatment. Furthermore, we compared the results to a state-of-the-art baseline approach based on an RGAT and DistMult encoder-decoder framework. Even though a direct comparison is difficult due to the different prediction properties of the models, the result of the baseline was used as guidance for performance comparison. Overall, our model achieved higher AUC-ROC values than the disease-specific baseline models. Additionally, besides the comparatively high prediction ability, the model is very flexible for new diseases, only requiring a small number of support patients. Since no disease-specific fine-tuning is required, the calculations can also be conducted very quickly. Finally, as a real-world application, we compared the three highest-ranked drugs recommended by our system against the actually prescribed drugs for one random example
Investigation of the Arabidopsis nonhost resistance mechanism against the soybean pathogen, Phytophthora sojae
No full textNonhost resistance (NHR) provides immunity to all members of a plant species against all isolates of a microorganism that is pathogenic to other plant species. Three Arabidopsis thaliana PEN (penetration deficient) genes, PEN1, 2 and 3 have been shown to provide prehaustorial NHR against the barley pathogen Blumeria graminis f. sp. hordei. Arabidopsis pen1-1 mutant is penetrated by the hemibiotrophic oomycete pathogen, Phytophthora sojae that causes root and stem rot disease in soybean. The P. sojae susceptible (pss) 1 mutant is infected by both P. sojae and the hemibiotrophic fungal pathogen, Fusarium virguliforme that causes sudden death syndrome in soybean. Thus, a common Arabidopsis NHR mechanism is functional against both hemibiotrophic oomycete and fungal pathogens of soybean. PSS1 encodes a glycine-rich protein (GRP), named GRP1, with no known function. Transformation of the soybean cultivar Williams 82 with AtGRP1 conferred enhanced resistance to both P. sojae and F. virguliforme. My study established that nonhost resistance genes are ideal for engineering broad-spectrum disease resistance in crop plants.</p
Dataset of miRNA-disease relations extracted from textual data using transformer-based neural networks
No full textMadan S, Kühnel L, Frohlich H, Hofmann-Apitius M, Fluck J. Dataset of miRNA-disease relations extracted from textual data using transformer-based neural networks. Database : the journal of biological databases and curation. 2024;2024.MicroRNAs (miRNAs) play important roles in post-transcriptional processes and regulate major cellular functions. The abnormal regulation of expression of miRNAs has been linked to numerous human diseases such as respiratory diseases, cancer, and neurodegenerative diseases. Latest miRNA-disease associations are predominantly found in unstructured biomedical literature. Retrieving these associations manually can be cumbersome and time-consuming due to the continuously expanding number of publications. We propose a deep learning-based text mining approach that extracts normalized miRNA-disease associations from biomedical literature. To train the deep learning models, we build a new training corpus that is extended by distant supervision utilizing multiple external databases. A quantitative evaluation shows that the workflow achieves an area under receiver operator characteristic curve of 98% on a holdout test set for the detection of miRNA-disease associations. We demonstrate the applicability of the approach by extracting new miRNA-disease associations from biomedical literature (PubMed and PubMed Central). We have shown through quantitative analysis and evaluation on three different neurodegenerative diseases that our approach can effectively extract miRNA-disease associations not yet available in public databases. Database URL: https://zenodo.org/records/10523046. © The Author(s) 2024. Published by Oxford University Press
Query optimization in mobile environments
No full textWe consider the issue of optimizing queries for distributed processing in mobile environment. An interesting characteristic of mobile machines is that they depend on battery as a source of energy which may not be substantial enough. Hence, the appropriate optimization criterion in a mobile environment considers both resource utilization and energy consum- ption at the mobile client.
In this scenario, the optimal plan for a query depends on the residual battery level of the mobile client and the load at the server. We approach this problem by compiling a query into a sequence of candidate plans, such that for any state of the client-server system, the optimal plan is one of the candidate plans.
A general solution is proposed by adapting the partial order dynamic programming search algorithm (p.o dp) such that the coverset of the query is the set of candidate plans. We propose two novel algorithms, namely, the linear combinations algorithm and the linearset algorithm (referred to as the linear algorithms) that compute the linearset of a query. The linear- set of a query is an approximation to the coverset returned by p.o. dp.
We show, by means of simulation, that (1) the linearset is an excellent approximation of the coverset, (2) query compilation using the linear algorithms outperform query compilation using p.o. dp by factors ranging from 2 to 9, (3) the time taken to compile queries using the linear algorithms for the general optimization criterion is at most twice the time taken by a System R* like standard query optimizer search algorithm, and (4) the run time overhead incurred by the linear algorithms technique is minimal.
The techniques presented in the paper are of general applicability to multi-criterion optimization problems in distributed databases, where each criterion is an additive metric.Technical report lcsr-tr-21
Interactive machine learning for complex graphics selection
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 89-91).Modern vector graphics editors support the creation of a wonderful variety of complex designs and artwork. Users produce highly realistic illustrations, stylized representational art, even nuanced data visualizations. In light of these complex graphics, selections, representations of sets of objects that users want to manipulate, become more complex as well. Direct manipulation tools that artists and designers find accessible and useful for editing graphics such as logos and icons do not have the same applicability in these more complex cases. Given that selection is the first step for nearly all editing in graphics, it is important to enable artists and designers to express these complex selections. This thesis explores the use of interactive machine learning techniques to improve direct selection interfaces. To investigate this approach, I created Insight, an interactive machine learning selection tool for making a relevant class of complex selections: visually similar objects. To make a selection, users iteratively provide examples of selection objects by clicking on them in the graphic. Insight infers a selection from the examples at each step, allowing users to quickly understand results of actions and reactively shape the complex selection. The interaction resembles the direct manipulation interactions artists and designers have found accessible, while helping express complex selections by inferring many parameter changes from simple actions. I evaluated Insight in a user study of digital designers and artists, finding that Insight enabled users to effectively and easily make complex selections not supported by state-of-the-art vector graphics editors. My results contribute to existing work by both indicating a useful approach for providing complex representation access to artists and designers, and showing a new application for interactive machine learning.by Sumit Gogia.M. Eng
Optimizing queries for coarse grain parallelism
No full textWe consider the problem of optimizing select-project-join relational queries for minimum response time on parallel machines. The design of the optimizer is based on three ideas: (1) the concept and quantification of degree of coarse grain parallelism for an execution tree, (2) the design of a parallelizing scheduler for a tree of coarse grain operations which is provably near optimal, and (3) the analysis of the scheduling algorithm to obtain a cost formula for parallel execution time. The search algorithm of the optimizer is presented as a multi-dimensional dynamic programming algorithm. We present two three- dimensional search algorithms for the case when placement of relations in the parallel machine do not overlap. We propose the tree placement strategy and demonstrate, by means of examples, how the number of dimensions in the search can be significantly reduced, thereby increasing the efficiency of the search algorithm.Technical report lcsr-tr-21
Redox-Responsive Nanocapsules for the Spatiotemporal Release of Miltefosine in Lysosome: Protection against Leishmania
No full textLeishmaniasis, a vector-borne disease, is caused by intracellular parasite Leishmania donovani. Unlike most intracellular pathogens, Leishmania donovani are lodged in parasitophorous vacuoles and replicate within the phagolysosomes in macrophages. Effective vaccines against this disease are still under development, while the efficacy of the available drugs is being questioned owing to the toxicity for nonspecific distribution in human physiology and the reported drug-resistance developed by Leishmania donovani. Thus, a stimuli-responsive nanocarrier that allows specific localization and release of the drug in the lysosome has been highly sought after for addressing two crucial issues, lower drug toxicity and a higher drug efficacy. We report here a unique lysosome targeting polymeric nanocapsules, formed via inverse mini-emulsion technique, for stimuli-responsive release of the drug miltefosine in the lysosome of macrophage RAW 264.7 cell line. A benign polymeric backbone, with a disulfide bonding susceptible to an oxidative cleavage, is utilized for the organelle-specific release of miltefosine. Oxidative rupture of the disulfide bond is induced by intracellular glutathione (GSH) as an endogenous stimulus. Such a stimuli-responsive release of the drug miltefosine in the lysosome of macrophage RAW 264.7 cell line over a few hours helped in achieving an improved drug efficacy by 200 times as compared to pure miltefosine. Such a drug formulation could contribute to a new line of treatment for leishmaniasis.A. Das acknowledges SERB (India) Grants (CRG/2020/000492 and JCB/2017/000004) and DBT Grant (BT/PR22251/NNT/28/1274/2017) for supporting this research. N. Mukherjee acknowledges SERB (India) Grant PDF/2016/001437 and K. Das acknowledges the grant EMR/2015/001674 for supporting this research. Financial support from DST (DST/INSPIRE/03/2017/002477) is acknowledged by R.T. This manuscript bears CSMCRI registration no 7/2021.Pramanik, SK (corresponding author), CSIR Cent Salt & Marine Chem Res Inst, Bhavnagar 364002, Gujarat, India.
Mukherjee, N (corresponding author), CSIR Indian Inst Chem Biol, Canc Biol & Inflammatory Disorder Div, Kolkata 700032, India.
Chattopadhy, S (corresponding author), BITS Pilani, Pilani 403726, Goa, India.
Das, A (corresponding author), Indian Inst Sci Educ & Res Kolkata, Mohanpur 741246, W Bengal, India.
[email protected]; [email protected]; [email protected]
Fast search methods for biological sequence databases
No full textBiology researchers have a pressing need for data management technologies which will make the storage and retrieval of DNA and protein sequence data accurate and efficient. The volume of data generated by DNA sequencing is already massive and will continue to grow rapidly. Even if the current sequence databases are adequate today, they most assuredly will become inadequate in the future when far more sequence data has been determined. The direction of future research in sequence databases needs to be in the organization of information. This is so that the volume of data needing to be searched does not grow linearly with the volume of sequence data being discovered.
We propose to develop an index structure and retrieval system called PROXIMAL for biological sequence databases which promises to be efficient and general. This organization of the databases will complement other current efforts at sequence comparison and analysis, by providing an infrastructure in which other methods can be used to efficiently locate desired sequences. Our method relies on the use of reference strings to partition the database of sequences. It is efficient since the use of multiple reference strings for any given distance measure greatly reduces the number of sequences that must be examined, allowing us to quickly locate sequences based on a precomputed metric. It is general since multiple distance measures can be used. These include at least differing gap and mismatch weights for the basic edit distance calculation, or entirely different models of mutation. The only requirement is that there is a metric structure - mainly, that the calculations satisfy the triangle inequality. This is a weak requirement that is satisfied by many interesting measures, including those currently in wide use for sequence comparison.Technical report LCSR-TR-21
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