Array Platform Research Articles

PD-1 Overexpression in Sézary Syndrome Is Epigenetically Regulated

PD-1 Overexpression in Sézary Syndrome Is Epigenetically Regulated

LEAP: Lightweight and Efficient Accelerator for Sparse Polynomial Multiplication of HQC

The Hamming quasi-cyclic (HQC) code-based encryption scheme is one of the fourth-round algorithms selected by the National Institute of Standards and Technology (NIST) postquantum cryptography (PQC) standardization process. However, very few hardware implementations have been reported for HQC to date. In this brief, we propose a novel Lightweight and Efficient Accelerator for sparse Polynomial multiplication (LEAP) of HQC, compatible with different parameters, on the field-programmable gate array (FPGA) platform. First, we give a mathematical derivation process for the sparse polynomial multiplication deployed in HQC. Then, we explain the proposed hardware structure in detail. Finally, we present the FPGA implementation results to confirm the efficiency of the proposed LEAP, for example, the proposed design for hqc-192 has at least 31.03% less area-delay product (ADP) than the existing design. LEAP can be extended further to construct efficient HQC cryptoprocessors.

Secure and efficient implementation of facial emotion detection for smart patient monitoring system

BackgroundMachine learning has enabled the automatic detection of facial expressions, which is particularly beneficial in smart monitoring and understanding the mental state of medical and psychological patients. Most algorithms that attain high emotion classification accuracy require extensive computational resources, which either require bulky and inefficient devices or require the sensor data to be processed on cloud servers. However, there is always the risk of privacy invasion, data misuse, and data manipulation when the raw images are transferred to cloud servers for processing facical emotion recognition (FER) data. One possible solution to this problem is to minimize the movement of such private data.MethodsIn this research, we propose an efficient implementation of a convolutional neural network (CNN) based algorithm for on‐device FER on a low‐power field programmable gate array (FPGA) platform. This is done by encoding the CNN weights to approximated signed digits, which reduces the number of partial sums to be computed for multiply‐accumulate (MAC) operations. This is advantageous for portable devices that lack full‐fledged resource‐intensive multipliers.ResultsWe applied our approximation method on MobileNet‐v2 and ResNet18 models, which were pretrained with the FER2013 dataset. Our implementations and simulations reduce the FPGA resource requirement by at least 22% compared to models with integer weight, with negligible loss in classification accuracy.ConclusionsThe outcome of this research will help in the development of secure and low‐power systems for FER and other biomedical applications. The approximation methods used in this research can also be extended to other image‐based biomedical research fields.

Plasma Aβ42/Aβ40 and p‐tau181 are associated with clinical progression in amnestic mild cognitive impairment: An 8‐year longitudinal study

AbstractBackgroundPrevious studies indicated that blood‐based biomarkers could predict cognitive decline in Alzheimer’s disease (AD) continuum.MethodTwo hundred and fifty‐one participants with amnestic mild cognitive impairment (aMCI) from the Shanghai Memory Study were followed up for a maximum of 8 years. Baseline blood biomarkers were measured with the single‐molecule array (Simoa) platform. Multipoint clinical diagnosis and domain‐specific cognitive functions were assessed to investigate the longitudinal relationship between blood biomarkers and clinical AD progression.ResultParticipants with high‐risk plasma Aβ42/Aβ40 (A) and p‐tau181 (T) level demonstrated the highest probability of incident AD (HR 5.54, 95% CI 2.99‐10.27), and the most dramatic decline in global cognition, attention, executive function, visuospatial function, and language. Comparing to young‐old participants, the old‐old ones with low‐ and moderate‐risk AT showed higher AD risks (HR 3.70, 95% CI 1.16‐11.81; HR 3.15, 95% CI 1.37‐7.26), and faster cognitive deterioration.ConclusionThe results supported the use of plasma Aβ42/Aβ40 and p‐tau181 as accessible and feasible indicators of AD progression and the long‐term cognitive deterioration, especially in patients with older age.

Implementation of Fully-Pipelined CNN Inference Accelerator on FPGA and HBM2 Platform

Many deep convolutional neural network (CNN) inference accelerators on the field-programmable gate array (FPGA) platform have been widely adopted due to their low power consumption and high performance. In this paper, we develop the following to improve performance and power efficiency. First, we use a high bandwidth memory (HBM) to expand the bandwidth of data transmission between the off-chip memory and the accelerator. Second, a fully-pipelined manner, which consists of pipelined inter-layer computation and a pipelined computation engine, is implemented to decrease idle time among layers. Third, a multi-core architecture with shared-dual buffers is designed to reduce off-chip memory access and maximize the throughput. We designed the proposed accelerator on the Xilinx Alveo U280 platform with in-depth Verilog HDL instead of high-level synthesis as the previous works and explored the VGG-16 model to verify the system during our experiment. With a similar accelerator architecture, the experimental results demonstrate that the memory bandwidth of HBM is 13.2× better than DDR4. Compared with other accelerators in terms of throughput, our accelerator is 1.9×/1.65×/11.9× better than FPGA+HBM2 based/low batch size (4) GPGPU/low batch size (4) CPU. Compared with the previous DDR+FPGA/DDR+GPGPU/DDR+CPU based accelerators in terms of power efficiency, our proposed system provides 1.4-1.7×/1.7-12.6×/6.6-37.1× improvement with the large-scale CNN model.

Immune landscape of patients with metastatic non-clear cell renal cell carcinoma (nccRCC) treated with atezolizumab plus bevacizumab.

4535 Background: The treatment of metastatic nccRCC remains an area of unmet clinical need. A multicenter phase II trial of atezolizumab/bevacizumab in patients with advanced nccRCC or any RCC type with ≥ 20% sarcomatoid differentiation (NCT02724878) demonstrated clinical activity of these agents, with objective response rates of 50% in patients with sarcomatoid clear-cell RCC (sccRCC) and 26% in patients with nccRCC. We aim to understand the intratumoral and circulating factors that impact immunotherapy response in this population. Methods: We collected blood samples prior to therapy and on-treatment (following 2 cycles of therapy) and quantified levels of plasma soluble factors (cytokines and growth factors) using a fluorescent bead array platform. We used high dimensional flow cytometry on peripheral blood mononuclear cells to analyze the composition and phenotype of peripheral immune cells. Immunohistochemistry and immunofluorescence were performed on available formalin-fixed, paraffin-embedded tumor tissue samples (archival or on-study biopsy) collected prior to the start of therapy. We assessed relationships between biomarkers and outcomes using pairwise Wilcoxon rank-sum test and log-rank tests, and univariate Cox regression models. Results: We collected blood samples from 60 patients with paired tumor samples from 38 patients. Baseline circulating inflammatory cytokines MIP-1b, IL-1, MCP-1, IL-6, and IL-13 were highly correlated with one another, forming an “inflammatory module” that was increased in IMDC-poor risk patients and associated with worse progression-free survival (PFS) (p = 0.028). At baseline, elevated circulating VEGF-A was associated with a lack of response (p=0.03) and worse PFS (p=0.021). Whereas a larger increase in on-treatment levels of circulating VEGF-A was associated with clinical benefit as defined by Braun et al. (Nat Med. 2020) (p=0.01) and improved overall survival (p=0.006). Among peripheral immune cell populations, an on-treatment decrease in circulating PD-L1+ T cells was associated with improved outcomes: reductions in CD4+ PD-L1+ (HR:0.62[0.49-0.91], p=0.016) and CD8+ PD-L1+ T cells (HR:0.59[0.39-0.87], p=0.009) correlated with improved PFS. Within the tumor, a higher percentage of terminally exhausted (PD-1+, TIM-3+ and/or LAG-3+) CD8+ T cells at baseline was associated with worse PFS (p=0.028). Conclusions: Among patients with advanced nccRCC or sccRCC, a baseline high systemic inflammatory state and high circulating VEGF-A are associated with worse outcomes after treatment with atezolizumab and bevacizumab. High levels of intratumor terminally exhausted CD8+ T cells were associated with worse PFS. These results highlight the importance of an integrative approach to explore biomarkers of response in advanced variant histology RCC which may inform future therapeutic approaches.

Head‐to‐head comparison of plasma biomarkers across the AD continuum in an Australian population

AbstractBackgroundPlasma amyloid‐β (Aβ) 1‐42/Aβ1‐40 ratio, phosphorylated‐tau181 (p‐tau181), glial fibrillary acidic protein (GFAP) and neurofilament light (NFL) are putative blood‐based biomarkers for Alzheimer’s disease (AD). However, head‐to‐head comparisons of the afore‐mentioned biomarkers across the AD continuum are lacking.MethodPlasma Aβ1‐42, Aβ1‐40, p‐tau181, GFAP and NFL were measured using the Single Molecule Array platform and compared cross‐sectionally in models with and without AD risk factors (age, sex and APOE ε4 status) across the AD continuum in Aβ PET positive confirmed participants (CU Aβ+, n = 39; MCI Aβ+, n = 33; AD Aβ+, n = 46) and Aβ PET negative confirmed participants (CU Aβ‐, n = 81; MCI Aβ‐, n = 26) from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) cohort. Area under the receiver operating characteristic curves (AUCs) for predicting Aβ PET‐/+ status were compared using DeLong test. Associations between plasma biomarker levels at baseline and prospective cognitive decline (CDR‐SOB) and brain Aβ load were also assessed.ResultLower plasma Aβ1‐42/Aβ1‐40 ratio and higher p‐tau181 and GFAP were observed in preclinical AD (Aβ1‐42/Aβ1‐40 ratio: ∼19%; p‐tau181: ∼70%; GFAP: ∼52%), prodromal AD (Aβ1‐42/Aβ1‐40 ratio: ∼14%; p‐tau181: ∼69%; GFAP: ∼45%) and AD (Aβ1‐42/Aβ1‐40 ratio: ∼16%; p‐tau181: ∼91%; GFAP: ∼85%), and higher plasma NFL was observed in prodromal AD (∼27%) and AD (∼45%) (Figure 1). Based on the AUCs, p‐tau181 performed equivalent to or better than (≥) other biomarkers, in predicting Aβ PET‐/+ status across the AD continuum (p‐tau AUCs for CU Aβ‐ vs CU Aβ+ ∼81%/ MCI Aβ+ ∼86%/ AD ∼92%). A biomarker panel of Aβ1‐42/Aβ1‐40 ratio, p‐tau181 and GFAP performed ≥ the biomarkers alone in predicting Aβ PET‐/+ status across the AD continuum (biomarker panel AUCs for CU Aβ‐ vs CU Aβ+ 90%/ MCI Aβ+ 89%/ AD 96%). Higher p‐tau181 (β = 0.531, p = 3.18E‐08), GFAP (β = 0.530, p = 5.07E‐08) and NFL (β = 0.487, p = 4.76E‐06) were associated with cognitive decline prospectively and lower plasma Aβ1‐42/Aβ1‐40 ratio (β = ‐6.035, p = 1.56E‐04) and higher p‐tau181 (β = 2.823, p = 4.72E‐05) and GFAP (β = 2.075, p = 0.003) were associated with increased Aβ PET load prospectively.ConclusionThese findings suggest that plasma biomarkers are altered across the AD continuum and are associated with cognitive decline and increased brain Aβ load prospectively.

Optimized Deep Learning-Based Fully Resolution Convolution Neural Network for Breast Tumour Segmentation on Field Programmable Gate Array

ABSTRACT Deep learning (DL) approaches have been highly interesting in segmentation and classification in recent years. During breast cancer detection, a convolutional neural network (CNN) requires several up-sampling operations to recover the original image from the feature map. This research introduces an optimised fully resolution-CNN (FR-CNN) based breast tumour segmentation in the field programmable gate array (FPGA) platform. The FPGA implementation of FR-CNN considers both fixed and floating point operations to find the best trade-off between accuracy and hardware complexity. The FR-CNN network model usually requires several adder and multiplier units that consume more power and area. Hence, an optimised Vedic multiplier based on a carry select adder with Simplified Sum-Carry Generation Logic (VCSA-SSCGL) is introduced. In addition, the particle swarm optimisation algorithm (PSO) is introduced for tuning the parameters in the network model. In the experimental scenario, the proposed model achieved an accuracy of 96.89%, precision of 95.84%, F-score of 96.08%, specificity of 96.73%, mean absolute error (MAE) of 0.87, dice similarity coefficient (DSC) of 0.93, and Jaccard coefficient (JC) of 0.9. Also, the FPGA design of a proposed model consumed only 0.6124W power and a LUT of 12,167. The experimental results prove the efficiency of a proposed method.

Review on FPGA-based accelerators in convolutional neural network

In current research, an important means to realize artificial intelligence is artificial neural network. Many tedious problemscan be solved by artificial neural network, such as image classification, speech recognition, natural language processing. Among neural networks, although convolutional neural network has significantly better performance in the field of image recognition, it requires many parameters and a large amount of computation, and the number of network layers is gradually increasing with the progress of the algorithm, which leads to model sizes are getting larger and more difficult to handle, which has more stringent requirements on various aspects of hardware capabilities, such as computing power, data storage and memory bandwidth. Therefore, the research of acceleration is particularly important, especially in the field of hardware acceleration. And as an emerging hardware platform, field programmable gate array has the characteristics of rapid customization and programmability, which can greatly improve the efficiency of accelerators design, implementation, and verification. As a result, it can be widely used in the hardware-accelerated design of neural networks. Although field programmable gate array itself has some defects, many research results have proved its huge advantages and development potential. This paper mainly summarizes the acceleration of convolutional neural network based on field programmable gate array platform, and discusses its characteristics and application space by comparing with other platforms, showing its high applicability to convolutional neural network, and finally looking forward to the progress of related research work.

Field‐programmable gate array acceleration of the Tersoff potential in LAMMPS

AbstractMolecular dynamics simulation is a common method to help humans understand the microscopic world. The traditional general‐purpose high‐performance computing platforms are hindered by low computational and power efficiency, constraining the practical application of large‐scale and long‐time many‐body molecular dynamics simulations. In order to address these problems, a novel molecular dynamics accelerator for the Tersoff potential is designed based on field‐programmable gate array (FPGA) platforms, which enables the acceleration of LAMMPS using FPGAs. Firstly, an on‐the‐fly method is proposed to build neighbor lists and reduce storage usage. Besides, multilevel parallelizations are implemented to enable the accelerator to be flexibly deployed on FPGAs of different scales and achieve good performance. Finally, mathematical models of the accelerator are built, and a method for using the models to determine the optimal‐performance parameters is proposed. Experimental results show that, when tested on the Xilinx Alveo U200, the proposed accelerator achieves a performance of 9.51 ns/day for the Tersoff simulation in a 55,296‐atom system, which is a 2.00 increase in performance when compared to Intel I7‐8700K and 1.70 to NVIDIA Tesla K40c under the same test case. In addition, in terms of computational efficiency and power efficiency, the proposed accelerator achieves improvements of 2.00 and 7.19 compared to Intel I7‐8700K, and 4.33 and 2.11 compared to NVIDIA Titan Xp, respectively.

Effect of preventive training in cardiac transcriptoma in rats with pulmonary hypertension

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Pulmonary hypertension (PH) is a progressive disease characterized by elevated right ventricular afterload, vascular remodeling, right ventricular hypertrophy and dysfunction, and ultimately heart failure. Physical exercise has been recognized as a treatment for this condition. Although different mechanisms have been proposed to explain PH development, the influence of preventive exercise training interventions onset of pulmonary hypertension in right ventricular hypertrophy is underexplored. Purpose Analyze the effect of preventive training in global gene expression profiling of right ventricular in PH from rats. Methods Twenty-four male Wistar rats (206-220 g) were divided into three four groups (n=8/group): S, sedentary control; SPH, sedentary pulmonary hypertension; and TPH, trained pulmonary hypertension group. The preventive training protocol was performed on a treadmill for 13 weeks, five times/week. Cardiac hypertrophy by PH was experimentally induced by a single intraperitoneal injection (60 mg/kg) of monocrotaline after the protocol training. The S were injected with saline solution. The rats were evaluated 28 days after monocrotaline administration, in the early stage of the PH. Left ventricle weight, right ventricle weight, and atrium weight normalized by body weight were used as indexes of heart hypertrophy. Gene expression profiling was performed using the Rat Gene ST Array platform. To further understand the biological relevance of differential expressed genes, we performed a functional enrichment analysis in the context of the gene ontology (biological process, molecular function, and cellular component) and interaction networks (KEGG, Reactome, Wikipathways and BioPlanet). Results Through the analysis, the HP group had cardiac hypertrophy, and the training mitigated this change p< 0.05. Global gene expression profiling identified 687 differentially expressed genes (S vs. SPH), in the hypertrophy right ventricular during PH, and when compared to preventive training, 91 genes were dysregulated (SPH vs TPH). Gene ontology analysis on expression profiling data revealed alteration on genes that regulate the inflammatory process, muscle growth, metabolic processes, cell death, and ion regulation comparison between SPH vs. TPH The genes that were altered in the hypertrophy phase of PH and modified by the exercise were platelet alpha granule lumen (genes Fgg and Qsox1), NAD + binding (gene Cryl1), positive regulation of cAMP-mediated signaling (gene Cxcl11), positive regulation of macrophage differentiation (Il34 gene), mRNA cleavage (Cstf3 gene), negative regulation of transporter activity (Wwp2 gene), regulation of respiratory system process (Mtg2 gene) p< 0.05. Conclusion Preventive training influences changes in cardiac gene expression profile in the hypertrophy stage of PH. New genes have been identified, and these could be biomarkers and potential therapeutic targets in the early stage of PH.

NKX3.1 immunohistochemistry and methylome profiling in mesenchymal chondrosarcoma: additional diagnostic value for a well-defined disease?

Mesenchymal chondrosarcoma (MCS) is a rare and highly aggressive tumour of soft tissue and bone that is defined by an underlying and highly specific fusion transcript involving HEY1 and NCOA2. Histologically, the tumours show a biphasic appearance consisting of an undifferentiated blue and round cell component as well as islands of highly differentiated cartilage. Particularly in core needle biopsies, the chondromatous component can be missed and the non-specific morphology and immunophenotype of the round cell component can cause diagnostic challenges. We applied NKX3.1 immunohistochemistry which was recently reported as a highly specific marker as well as methylome and copy number profiling to a set of 45 well characterised MCS cases to evaluate their potential diagnostic value. Methylome profiling revealed a highly distinct cluster for MCS. Notably, the findings were reproducible also when analysing the round cell and cartilaginous component separately. Furthermore, four outliers were identified by methylome profiling for which the diagnosis had to be revised. NKX3.1 immunohistochemistry showed positivity in 36% of tumours, the majority of which was rather focal and weak. Taken together, NKX3.1 expression showed a low sensitivity but a high specificity in our analysis. Methylome profiling on the other hand represents a sensitive, specific and reliable tool to support the diagnosis of MCS, particularly if only the round cell component is obtained in a biopsy and the diagnosis is not suspected. Furthermore, it can aid in confirming the diagnosis in case RNA sequencing for the HEY1::NCOA2 fusion transcript is not available.

Implementation of polyphase digital down converter for wireless applications

This paper briefs the hardware-efficient polyphase digital down converter (DDC), which reduces the input sampling frequency to 3.64 GHz and produces a complex output to meet the IEEE 802.11ah wireless network standard. The design comprises a polyphase mixer (PM) filter and a lowpass polyphase finite impulse response (PFIR) filter. The PM filter uses a cascaded integrator comb (CIC) decimation filter based on multiplexer technology to potentially process parallel input interfaces and considerably reduce the sampling rate. The time-sharing mechanism of the CIC filter performs an efficient hardware implementation. Again, the improved design technique of the PFIR filter can effectively increase the operating speed and optimize power consumption. The proposed design offers high flexibility due to programmable sampling rate factors that can support multistandard communication systems. In addition, the design includes the truncation mechanism to protect against overflow errors and the fixed-point data type in each filter node to minimize the area requirements. The presented polyphase DDC is tested using the field-programmable gate array (FPGA) platform targeting the Xilinx Kintex-7 device. According to experimental results, the described DDC significantly reduces the logical resources and power dissipation associated with earlier design structures. The functional verification is tested to ensure the validation of the suggested design.

An efficient FPGA‐based implementation of UWB radar system for through‐wall imaging

SummaryThe work presented in this paper aims to develop a low‐cost ultra‐wideband (UWB) radar system that has the capability to image through the wall of a human target. The proposed radar system relies heavily on a field‐programmable gate array (FPGA) platform. The Xilinx FPGA Kintex‐7 (KC705) board is employed to integrate the main functionalities of the radar system, such as the generation and acquisition of a UWB signal. The generated signal is a monocycle signal, which has an ultra‐wide bandwidth. Due to the UWB nature of this signal, the radar system achieves better penetration ability, as well as high imaging resolution. However, the major challenge of a UWB radar system lies in the stage of digitization, as it requires a high sampling rate analog‐to‐digital converter (ADC). In fact, such a chip is very expensive. Hence, to reach a high sampling resolution using a low‐cost and low‐speed ADC, an efficient sampling strategy is implemented. In contrast to other UWB sampling methods, which require a hardware delay line chip, the new sampling scheme depends only on the FPGA firmware to realize a combination of real‐time and equivalent‐time sampling, which provides better jitter performance. Finally, to demonstrate the imaging capability, experimental tests are conducted in an indoor environment while human targets are located in different places. The measurement results revealed that the proposed radar system has the ability to provide 2D images that accurately determine the location of the target.

A microdots array-based fluoremetric assay with superwettability profile for simultaneous and separate analysis of iron and copper in red wine.

A microdots array-based fluoremetric method with superwettability profile has been developed for the simultaneous and separate detection of Fe3+ and Cu2+ ions in red wine samples. A wettable micropores array was initially designed with high density by using polyacrylic acid (PAA) and hexadecyltrimethoxysilane (HDS), followed by the NaOH etching route. Zinc metal organic frameworks (Zn-MOFs) were fabricated as the fluorescent probes to be immobilized into the micropores array to obtain the fluoremetric microdots array platform. It was found that the fluorescence of Zn-MOFs probes could decrease significantly in the presence of Fe3+ and/or Cu2+ ions towards their simultaneous analysis. Yet, the specific responses to Fe3+ ions could be expected if using histidine to chelate Cu2+ ions. Moreover, the developed Zn-MOFs-based microdots array with superwettability profile can enable the accumulation of targeting ions from the complicated samples without any tedious pre-processing. Also, the cross-contamination of different samples droplets can be largely avoided so as to facilitate the analysis of multiple samples. Subsequently, the feasibility of simultaneous and separate detection of Fe3+ and Cu2+ ions in red wine samples was demonstrated. Such a design of microdots array-based detection platform may promise the wide applications in analyzing Fe3+ and/or Cu2+ ions in the fields of food safety, environmental monitoring, and medical diseases diagnostics.

Selective Breeding for High Intrinsic Exercise Capacity Slows Pan-Tissue Epigenetic Aging in Rats

Aerobic exercise capacity is strongly associated with longevity and reduction in all-cause mortality. Rats selectively bred for high intrinsic running capacity (HCR) compared with low (LCR), have between a 28-45% increase in median lifespan. It is not known whether the greater HCR survival is associated with slowed epigenetic aging. We hypothesized that HCR rats would express altered rates of pan-tissue DNA methylation corresponding to a slowed epigenetic clock (DNAmAge). Male and female LCR and HCR rats from generation 15-17 (n=22; chronological age=2.34±0.45 yrs (mean±SD)), generation 28 (n=20; 1.09±0.05 yrs) and generation 37 (n=20; 1.04±0.08 yrs) were included. DNA was extracted from skeletal muscle (n=61), cardiac muscle (n=60), adipose (n=61) and liver (n=61). DNAmAge was quantitated using the mammalian methylation array platform (~106k CpGs specific to brown rat). Three epigenetic clocks were used, previously trained on separate rat tissues, including dual species clocks that apply to rats and humans (pan-tissue rat clock; human-rat clocks for relative and chronologic age). Slowing of epigenetic age relative to chronologic age was assessed by multivariable regression adjusted for chronologic age, generation, sex and phenotype (treadmill running distance at 3 months of age). Epigenome wide association with phenotype was performed in Limma, with Benjamini-Hochberg p-value correction and significance set at FDR q<0.05. Gene set enrichment analysis was performed with Gene Ontology Biological Processes. Maximum treadmill running distance was ~8 times greater in HCR than LCR (2,137±500 vs. 246±63 m; p=1.97✕10-20). Across all samples and clocks, epigenetic age was associated with chronological age (r=0.461-0.500; p<3.60✕10-14). Compared with LCR, HCR epigenetic aging was slowed by a mean of -0.18 to -0.24 yrs, which represents 9-42% lower DNAmAge in HCR rats than LCR (p<0.024). Tissue-specific analyses revealed a lower DNAmAge in HCR for adipose, skeletal muscle and cardiac muscle (pan-tissue rat clock; p<0.04), and liver (human-rat clock for relative and chronologic age; p<0.01); effects were strongest at younger chronologic age. Significantly differentially methylated CpGs were found in skeletal muscle (n=11,341), cardiac muscle (n=2,480), adipose (n=3,036) and liver (n=12,595). Of the top 10 differentially methylated pathways, ~58% related to tissue development and ~20% related to cardiac or skeletal muscle. These data show differences in the rate of epigenetic aging manifest in tissues that directly (skeletal and cardiac muscle) and indirectly (adipose, liver) contribute to the energy transfer of aerobic exercise capacity upon which the selective breeding of LCR and HCR rat models were based. These data suggest that a large fraction of the survival benefit associated with high intrinsic exercise capacity is reflected in slowed epigenetic aging across multiple tissues. The Lundquist Institute. Epigenetic Clock Development Foundation. The University of Toledo Department of Physiology & Pharmacology. NIH (R01HL151452, R01HL153460, P50HD098593, P40OD021331). Open Philanthropy (SH). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Creating Stretchable Electronics from Dual Layer Flex-PCB for Soft Robotic Cardiac Mapping Catheters

The authors present in this study the development of a novel method for creating stretchable electronics from dual-layer flex printed circuit boards (flex-PCBs) as a platform for soft robotic sensor arrays (SRSAs) for cardiac voltage mapping applications. There is a crucial need for devices that utilize multiple sensors and provide high performance signal acquisition for cardiac mapping. Previously, our group demonstrated how single-layer flex-PCB can be postprocessed to create a stretchable electronic sensing array. In this work, a detailed fabrication process for creating a dual-layer multielectrode flex-PCB SRSA is presented, along with relevant parameters to achieve optimal postprocessing with a laser cutter. The dual-layer flex-PCB SRSA’s ability to acquire electrical signals is demonstrated both in vitro as well as in vivo on a Leporine cardiac surface. These SRSAs could be extended into full-chamber cardiac mapping catheter applications. Our results show a significant contribution towards the scalable use of dual-layer flex-PCB for stretchable electronics.

FPGA-Flux Proprietary System for Online Detection of Outer Race Faults in Bearings

Online fault detection in industrial machinery, such as induction motors or their components (e.g., bearings), continues to be a priority. Most commercial equipment provides general measurements and not a diagnosis. On the other hand, commonly, research works that focus on fault detection are tested offline or over processors that do not comply with an online diagnosis. In this sense, the present work proposes a system based on a proprietary field programmable gate array (FPGA) platform with several developed intellectual property cores (IPcores) and tools. The FPGA platform together with a stray magnetic flux sensor are used for the online detection of faults in the outer race of bearings in induction motors. The integrated parts comprising the monitoring system are the stray magnetic flux triaxial sensor, several developed IPcores, an embedded processor for data processing, and a user interface where the diagnosis is visualized. The system performs the fault diagnosis through a statistical analysis as follows: First, a triaxial sensor measures the stray magnetic flux in the motor’s surroundings (this flux will vary as symptoms of the fault). Second, an embedded processor in an FPGA-based proprietary board drives the developed IPcores in calculating the statistical features. Third, a set of ranges is defined for the statistical features values, and it is used to indicate the condition of the bearing in the motor. Therefore, if the value of a statistical feature belongs to a specific range, the system will return a diagnosis of whether a fault is present and, if so, the severity of the damage in the outer race. The results demonstrate that the values of the root mean square (RMS) and kurtosis, extracted from the stray magnetic field from the motor, provide a reliable diagnostic of the analyzed bearing. The results are provided online and displayed for the user through interfaces developed on the FPGA platform, such as in a liquid crystal display or through serial communication by a Bluetooth module. The platform is based on an FPGA XC6SLX45 Spartan 6 of Xilinx, and the architecture of the modules used are described through hardware description language. This system aims to be an online tool that can help users of induction motors in maintenance tasks and for the early detection of faults related to bearings.

Modulatory effects of real-time electromagnetic stimulation on epileptiform activity in juvenile rat hippocampus based on multi-electrode array recordings

Electromagnetic stimulation (EMS) has proven to be useful for the focal suppression of epileptiform activity (EFA) in the hippocampus. There is a critical period during EFA for achieving the transition from brief interictal discharges (IIDs) to prolonged ictal discharges (IDs), and it is unknown whether EMS can modulate this transition. Therefore, this study aimed to evaluate the intensity- and time-dependent effect of EMS on the transition of EFA. A juvenile rat EFA model was constructed by perfusing magnesium-free artificial cerebrospinal fluid (aCSF) on brain slices, and the induced EFA was recorded using a micro-electrode array (MEA) platform. After a stable EFA event was recorded for some time, real-time pulsed magnetic stimulation with low and high peak-to-peak input magnetic field intensities was carried out. A 5-min intervention with real-time magnetic fields with low intensity was found to reduce the amplitude of IDs (ID events still existed), whereas a 5-min intervention with real-time magnetic fields with high input voltages completely suppressed IDs. Short-time magnetic fields (9 s and 1 min) with high or low input intensity had no effect on EFA. Real-time magnetic fields can block the normal EFA process from IIDs to IDs (i.e., a complete EFA cycle) and this suppression effect is dependent on input intensities and intervention duration. The experimental findings further indicate that magnetic stimulation may be chosen as an alternative antiepileptic therapy.

Impact of intrauterine exposure to maternal diabetes on preterm birth: fetal DNA methylation alteration is an important mediator

BackgroundIn utero exposure to diabetes has been shown to contribute to preterm birth, though the underlying biological mechanisms are yet to be fully elucidated. Fetal epigenetic variations established in utero may be a possible pathway. This study aimed to investigate whether in utero exposure to diabetes was associated with a change in newborn DNA methylation, and whether the identified CpG sites mediate the association between diabetes and preterm birth in a racially diverse birth cohort population.MethodsThis study included 954 mother–newborn pairs. Methylation levels in the cord blood were determined using the Illumina Infinium MethylationEPIC BeadChip 850 K array platform. In utero exposure to diabetes was defined by the presence of maternal pregestational or gestational diabetes. Preterm birth was defined as gestational age at birth less than 37 weeks. Linear regression analysis was employed to identify differentially methylated CpG sites. Differentially methylated regions were identified using the DMRcate Package.Results126 (13%) newborns were born to mothers with diabetes in pregnancy and 173 (18%) newborns were born preterm, while 41 newborns were born both preterm and to mothers with diabetes in pregnancy. Genomic-wide CpG analysis found that eighteen CpG sites in cord blood were differentially methylated by maternal diabetes status at an FDR threshold of 5%. These significant CpG sites were mapped to 12 known genes, one of which was annotated to gene Major Histocompatibility Complex, Class II, DM Beta (HLA-DMB). Consistently, one of the two identified significant methylated regions overlapped with HLA-DMB. The identified differentially methylated CpG sites mediated the association between diabetes in pregnancy and preterm birth by 61%.ConclusionsIn this US birth cohort, we found that maternal diabetes was associated with altered fetal DNA methylation patterns, which substantially explained the link between diabetes and preterm birth.