Today's computing challenges: opportunities for computer hardware design.
Ontology highlight
ABSTRACT:
SUBMITTER: Bae W
PROVIDER: S-EPMC8022507 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The efficacy of wireless intracortical brain-computer interfaces (iBCIs) is limited in part by the number of recording channels, which is constrained by the power budget of the implantable system. Designing wireless iBCIs that provide the high-quality recordings of today's wired neural interfaces may lead to inadvertent over-design at the expense of power consumption and scalability. Here, we report analyses of neural signals collected from experimental iBCI measurements in rhesus macaques and from a clinical-trial participant with implanted 96-channel Utah multielectrode arrays to understand the trade-offs between signal quality and decoder performance. Moreover, we propose an efficient hardware design for clinically viable iBCIs, and suggest that the circuit design parameters of current recording iBCIs can be relaxed considerably without loss of performance. The proposed design may allow for an order-of-magnitude power savings and lead to clinically viable iBCIs with a higher channel count.
| S-EPMC8286886 | biostudies-literature
Project description:Recent advances in immuno-oncology and regulatory approvals have been rapid and paradigm shifting in many difficult-to-treat malignancies. Despite immune checkpoint inhibitor therapy becoming the standard of care across multiple tumor types, there are many unanswered questions that need to be addressed before this therapeutic modality can be fully harnessed. Areas of limitations include treatment of patients not sufficiently represented in clinical trials, uncertainty of the optimal treatment dosing and duration, and lack of understanding regarding long-term immune related toxicities and atypical tumor responses. Patients such as those with autoimmune disease, chronic viral infections, limited performance status, and brain metastases were often excluded from initial trials due to concerns of safety. However, limited data suggest that some of these patients can benefit from therapy with manageable toxicities; thus, future studies should incorporate these patients to clearly define safety and efficacy. There are still controversies regarding the optimal dosing strategy that can vary from weight-based to flat dosing, with undefined treatment duration. Further elucidation of the optimal dosing approach and evaluation of predictive biomarkers should be incorporated in the design of future trials. Finally, there are long-term immune-mediated toxicities, atypical tumor responses such as pseudoprogression and endpoints unique to immuno-oncology that are not adequately captured by traditional trial designs; thus, novel study designs are needed. In this article, we discuss in detail the above challenges and propose needed areas of research for exploration and incorporation in the next generation of immuno-oncology clinical trials.
| S-EPMC5735832 | biostudies-literature
Project description:Immunotherapy adds an exciting new dimension to the treatment of cancer, joining other approaches as a key pillar in the oncotherapeutics armamentarium. Immuno-oncology agents harbor unique mechanisms of antitumor activity by leveraging the host immune system, which may result in response patterns, resistance kinetics, and toxicity profiles that differ from other systemic therapies. These features have led to many discussions on ways to optimally integrate immunotherapy into cancer clinical trials. This overview provides an introduction to the four CCR Focus articles that ensue, with special thoughts paid to clinical trial endpoints, biomarker development and validation, combination strategies, and limitations that arise with increasing use of these agents. In addition, this overview examines design concepts that may be applied to invigorate clinical trials and to maximize their impact in the immuno-oncology era.
| S-EPMC5669041 | biostudies-literature
Project description:Minimal hardware implementations able to cope with the processing of large amounts of data in reasonable times are highly desired in our information-driven society. In this work we review the application of stochastic computing to probabilistic-based pattern-recognition analysis of huge database sets. The proposed technique consists in the hardware implementation of a parallel architecture implementing a similarity search of data with respect to different pre-stored categories. We design pulse-based stochastic-logic blocks to obtain an efficient pattern recognition system. The proposed architecture speeds up the screening process of huge databases by a factor of 7 when compared to a conventional digital implementation using the same hardware area.
| S-EPMC4425430 | biostudies-literature
Project description:FPGAs have shown several characteristics that make them very attractive for high performance computing (HPC). The impressive speed-up factors that they are able to achieve, the reduced power consumption, and the easiness and flexibility of the design process with fast iterations between consecutive versions are examples of benefits obtained with their use. However, there are still some difficulties when using reconfigurable platforms as accelerator that need to be addressed: the need of an in-depth application study to identify potential acceleration, the lack of tools for the deployment of computational problems in distributed hardware platforms, and the low portability of components, among others. This work proposes a complete grid infrastructure for distributed high performance computing based on dynamically reconfigurable FPGAs. Besides, a set of services designed to facilitate the application deployment is described. An example application and a comparison with other hardware and software implementations are shown. Experimental results show that the proposed architecture offers encouraging advantages for deployment of high performance distributed applications simplifying development process.
| S-EPMC4385699 | biostudies-other
Project description:Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.
| S-EPMC4382991 | biostudies-literature
Project description:BACKGROUND:Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. With the increase in complexity of BRNs in terms of number of entities and their interactions, the task of parameters estimation becomes computationally expensive with existing sequential SMBioNET tool. We extend the existing sequential implementation of SMBioNET by using a data decomposition approach using a Java messaging library called MPJ Express. The approach divides the parameters space into different regions and each region is then explored in parallel on High Performance Computing (HPC) hardware. RESULTS:The performance of the parallel approach is evaluated on BRNs of different sizes, and experimental results on multicore and cluster computers showed almost linear speed-up. This parallel code can be executed on a wide range of concurrent hardware including laptops equipped with multicore processors, and specialized distributed memory computer systems. To demonstrate the application of parallel implementation, we selected a case study of Hexosamine Biosynthetic Pathway (HBP) in cancer progression to identify potential therapeutic targets against cancer. A set of logical parameters were computed for HBP model that directs the biological system to a state of recovery. Furthermore, the parameters also suggest a potential therapeutic intervention that restores homeostasis. Additionally, the performance of parallel application was also evaluated on a network (comprising of 23 entities) of Fibroblast Growth Factor Signalling in Drosophila melanogaster. CONCLUSIONS:Qualitative modeling framework is widely used for investigating dynamics of biological regulatory networks. However, computation of model parameters in qualitative modeling is computationally intensive. In this work, we presented results of our Java based parallel implementation that provides almost linear speed-up on both multicore and cluster platforms. The parallel implementation is available at https://psmbionet.github.io .
| S-EPMC6311083 | biostudies-other
Project description:Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.
| S-EPMC7023065 | biostudies-literature
Project description:Advances in computational geometric modeling, imaging, and simulation let researchers build and test models of increasing complexity, generating unprecedented amounts of data. As recent research in biomedical applications illustrates, visualization will be critical in making this vast amount of data usable; it's also fundamental to understanding models of complex phenomena.
| S-EPMC3336198 | biostudies-literature
Project description:Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.
2021-02-02 | GSE165961 | GEO