Browse
Submit Data
Databases
API
Help

Dataset Information

2 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Ultra-fast data sanitization of SRAM by back-biasing to resist a cold boot attack.

ABSTRACT: Although SRAM is a well-established type of volatile memory, data remanence has been observed at low temperature even for a power-off state, and thus it is vulnerable to a physical cold boot attack. To address this, an ultra-fast data sanitization method within 5 ns is demonstrated with physics-based simulations for avoidance of the cold boot attack to SRAM. Back-bias, which can control device parameters of CMOS, such as threshold voltage and leakage current, was utilized for the ultra-fast data sanitization. It is applicable to temporary erasing with data recoverability against a low-level attack as well as permanent erasing with data irrecoverability against a high-level attack.

SUBMITTER: Han SJ

PROVIDER: S-EPMC8742074 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Fast machine-learning online optimization of ultra-cold-atom experiments.

Project description:We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

| S-EPMC4867626 | biostudies-literature

Ultra-fast transcriptional responses in plants

Project description:Transcriptome of plants exposed to 0, 20 and 60 sec light stress was analyzed using Illumina HiSeq2000. We found that mRNA accumulation of more than 700 transcripts in plants occurs as early as 20-60 sec following light stress application

2016-12-06 | GSE60865 | GEO

Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche.

Project description:Immunotherapy is a promising treatment for triple-negative breast cancer (TNBC), but patients relapse, highlighting the need to understand the mechanisms of resistance. We discovered that in primary breast cancer, tumor cells that resist T cell attack are quiescent. Quiescent cancer cells (QCCs) form clusters with reduced immune infiltration. They also display superior tumorigenic capacity and higher expression of chemotherapy resistance and stemness genes. We adapted single-cell RNA-sequencing with precise spatial resolution to profile infiltrating cells inside and outside the QCC niche. This transcriptomic analysis revealed hypoxia-induced programs and identified more exhausted T cells, tumor-protective fibroblasts, and dysfunctional dendritic cells inside clusters of QCCs. This uncovered differential phenotypes in infiltrating cells based on their intra-tumor location. Thus, QCCs constitute immunotherapy-resistant reservoirs by orchestrating a local hypoxic immune-suppressive milieu that blocks T cell function. Eliminating QCCs holds the promise to counteract immunotherapy resistance and prevent disease recurrence in TNBC.

| S-EPMC11332067 | biostudies-literature

SRAM-Based PUF Readouts.

Project description:Large-scale parameter characterization of Physical Unclonable Functions (PUFs) is of paramount importance in order to assess the quality and thus the suitability of such PUFs which would then be developed as an industrial-grade solution for hardware root of trust. Carrying out a proper characterization requires a large number of devices that need to be repeatedly sampled at various conditions. These prerequisites make PUF characterization process a very time-consuming and expensive task. Our work presents a dataset for the study of SRAM-based PUFs on microcontrollers; it includes full SRAM readouts along with internal voltage and temperature sensors of 84 microcontrollers of STM32 type. Data has been gathered with a custom-made and open platform designed for the automatic acquisition of SRAM readouts of such devices. This platform also provides possibilities of experimenting aging and reliability properties.

| S-EPMC10224988 | biostudies-literature

The invasive pest Drosophila suzukii uses trans-generational medication to resist parasitoid attack.

Project description:Animal medication is a behavioral strategy to resist enemies based on the use of substances from the environment. While it has been observed in several animals, whether invasive species can use medication to resist new enemies during its expansion is unknown. Here, we show that the worldwide invasive pest Drosophila suzukii performs trans-generational prophylactic medication by adapting its oviposition behavior in the presence of enemies. We find that flies preferentially lay their eggs on media containing atropine - an entomotoxic alkaloid - in the presence of parasitoids. We further show that flies developing on atropine more efficiently resist parasitization by parasitoids. Finally, we find that developing in hosts reared on atropine strongly impacts the life-history traits of parasitoids. This protective behavior is reported for the first time in a pest and invasive species, and suggests that animal medication may be an important driver of population dynamics during invasions.

| S-EPMC5347128 | biostudies-literature

Ultra-fast deep-learned pediatric CNS tumor classification

Project description:In this study nanopore sequencing was applied to obtain sparse DNA methylation profiles from pediatric CNS tumor samples. A neural network was used to classify the tumor based on the obtained methylation profile.

2023-08-08 | GSE237874 | GEO

Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche

Project description:This SuperSeries is composed of the SubSeries listed below.

2022-04-20 | GSE198715 | GEO

Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche (PADMEseq)

Project description:Immunotherapy is a promising treatment for Triple-Negative Breast Cancer (TNBC), but patients recur, arising the need to understand mechanisms of resistance. We discovered that in primary breast cancer, tumor cells that survive T-cell attack are in a quiescent state. Quiescent Cancer Cells (QCCs) are found clustering together forming regions with reduced immune infiltration. QCCs display superior tumorigenic capacity and higher expression of stemness genes than their proliferative counterparts. We adapted single-cell-RNA-sequencing with precise spatial resolution to profile infiltrating cells inside and outside QCC niches. This transcriptomic analysis revealed hypoxia-induced programs and identified more exhausted T-cells, tumor-protective fibroblasts, and dysfunctional dendritic cells inside clusters of QCCs. This uncovered differential phenotypes in infiltrating cells based on their specific intra-tumor location. Thus, QCCs constitute immunotherapy-resistant reservoirs by orchestrating a local hypoxic immune-suppressive milieu that blocks T-cell function. Eliminating QCCs holds the promise to counteract resistance to immunotherapy and prevent disease recurrence in TNBC.

2022-04-20 | GSE198714 | GEO

β Cells that Resist Immunological Attack Develop during Progression of Autoimmune Diabetes in NOD Mice.

Project description:Type 1 diabetes (T1D) is a chronic autoimmune disease that involves immune-mediated destruction of β cells. How β cells respond to immune attack is unknown. We identified a population of β cells during the progression of T1D in non-obese diabetic (NOD) mice that survives immune attack. This population develops from normal β cells confronted with islet infiltrates. Pathways involving cell movement, growth and proliferation, immune responses, and cell death and survival are activated in these cells. There is reduced expression of β cell identity genes and diabetes antigens and increased immune inhibitory markers and stemness genes. This new subpopulation is resistant to killing when diabetes is precipitated with cyclophosphamide. Human β cells show similar changes when cultured with immune cells. These changes may account for the chronicity of the disease and the long-term survival of β cells in some patients.

| S-EPMC5342930 | biostudies-literature

An ultra-fast metabolite prediction algorithm.

Project description:Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.

| S-EPMC3380062 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data