42 research outputs found
TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation
Intelligent edge vision tasks encounter the critical challenge of ensuring
power and latency efficiency due to the typically heavy computational load they
impose on edge platforms.This work leverages one of the first "AI in sensor"
vision platforms, IMX500 by Sony, to achieve ultra-fast and ultra-low-power
end-to-end edge vision applications. We evaluate the IMX500 and compare it to
other edge platforms, such as the Google Coral Dev Micro and Sony Spresense, by
exploring gaze estimation as a case study. We propose TinyTracker, a highly
efficient, fully quantized model for 2D gaze estimation designed to maximize
the performance of the edge vision systems considered in this study.
TinyTracker achieves a 41x size reduction (600Kb) compared to iTracker [1]
without significant loss in gaze estimation accuracy (maximum of 0.16 cm when
fully quantized). TinyTracker's deployment on the Sony IMX500 vision sensor
results in end-to-end latency of around 19ms. The camera takes around 17.9ms to
read, process and transmit the pixels to the accelerator. The inference time of
the network is 0.86ms with an additional 0.24 ms for retrieving the results
from the sensor. The overall energy consumption of the end-to-end system is 4.9
mJ, including 0.06 mJ for inference. The end-to-end study shows that IMX500 is
1.7x faster than CoralMicro (19ms vs 34.4ms) and 7x more power efficient (4.9mJ
VS 34.2mJ
LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules
Many ncRNAs function through RNA-RNA interactions. Fast and reliable RNA
structure prediction with consideration of RNA-RNA interaction is useful. Some
existing tools are less accurate due to omitting the competing of
intermolecular and intramolecular base pairs, or focus more on predicting the
binding region rather than predicting the complete secondary structure of two
interacting strands. Vienna RNAcofold, which reduces the problem into the
classical single sequence folding by concatenating two strands, scales in cubic
time against the combined sequence length, and is slow for long sequences. To
address these issues, we present LinearCoFold, which predicts the complete
minimum free energy structure of two strands in linear runtime, and
LinearCoPartition, which calculates the cofolding partition function and base
pairing probabilities in linear runtime. LinearCoFold and LinearCoPartition
follows the concatenation strategy of RNAcofold, but are orders of magnitude
faster than RNAcofold. For example, on a sequence pair with combined length of
26,190 nt, LinearCoFold is 86.8x faster than RNAcofold MFE mode (0.6 minutes
vs. 52.1 minutes), and LinearCoPartition is 642.3x faster than RNAcofold
partition function mode (1.8 minutes vs. 1156.2 minutes). Different from the
local algorithms, LinearCoFold and LinearCoPartition are global cofolding
algorithms without restriction on base pair length. Surprisingly, LinearCoFold
and LinearCoPartition's predictions have higher PPV and sensitivity of
intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the
RNA-RNA interaction between SARS-CoV-2 gRNA and human U4 snRNA, which has been
experimentally studied, and observe that LinearCoFold's prediction correlates
better to the wet lab results
Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO
Smart glasses are rapidly gaining advanced functionality thanks to
cutting-edge computing technologies, accelerated hardware architectures, and
tiny AI algorithms. Integrating AI into smart glasses featuring a small form
factor and limited battery capacity is still challenging when targeting
full-day usage for a satisfactory user experience. This paper illustrates the
design and implementation of tiny machine-learning algorithms exploiting novel
low-power processors to enable prolonged continuous operation in smart glasses.
We explore the energy- and latency-efficient of smart glasses in the case of
real-time object detection. To this goal, we designed a smart glasses prototype
as a research platform featuring two microcontrollers, including a novel
milliwatt-power RISC-V parallel processor with a hardware accelerator for
visual AI, and a Bluetooth low-power module for communication. The smart
glasses integrate power cycling mechanisms, including image and audio sensing
interfaces. Furthermore, we developed a family of novel tiny deep-learning
models based on YOLO with sub-million parameters customized for
microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8, aiming
at benchmarking object detection with smart glasses for energy and latency.
Evaluations on the prototype of the smart glasses demonstrate TinyissimoYOLO's
17ms inference latency and 1.59mJ energy consumption per inference while
ensuring acceptable detection accuracy. Further evaluation reveals an
end-to-end latency from image capturing to the algorithm's prediction of 56ms
or equivalently 18 fps, with a total power consumption of 62.9mW, equivalent to
a 9.3 hours of continuous run time on a 154mAh battery. These results
outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image
classification) at just 7.3 fps per second
Association of n-3 polyunsaturated fatty acid intakes with juvenile myopia: A cross-sectional study based on the NHANES database
AimInflammation is involved in the development of myopia. n-3 polyunsaturated fatty acids (n-3 PUFAs) have vasodilating and anti-inflammatory effects, which may be involved in controlling myopia. It is of great significance to explore the relationship between n-3 PUFA intakes and juvenile myopia in order to control and alleviate myopia among teenagers through dietary intervention.MethodsSociodemographic data, information of nutrient intakes, cotinine, PUFAs, and eye refractive status of 1,128 juveniles were extracted from the National Health and Nutrition Examination Survey (NHANES) database in this cross-sectional study. PUFAs contained total polyunsaturated fatty acid (TPFAs), alpha-linolenic acid, octadecatetraenoic acid, eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA). Covariates were screened by comparison among groups of normal vision, low myopia, and high myopia. The association between n-3 PUFA intakes and the risk of juvenile myopia was evaluated using univariate and multivariate logistic regression analyses with odds ratios (ORs) and 95% confidence intervals (CIs).ResultsAmong the juveniles, 788 (70.68%) had normal vision, 299 (25.80%) had low myopia, and 41 (3.52%) had high myopia. There were significant differences in average EPA and DHA intakes among the three groups, and mean DPA and DHA intakes in the normal vision group were lower than those in the low myopia group (P < 0.05). After adjustment for age, gender, TPFAs, and cotinine, a high dietary intake of EPA (≥11 mg/1,000 kcal) in juveniles seemed to be associated with the risk of high myopia (OR = 0.39, 95% CI: 0.18–0.85), while no significant associations were identified between n-3 PUFA intakes and the risk of low myopia.ConclusionA high dietary intake of EPA may be associated with a decreased risk of high myopia among juveniles. A further prospective study is needed to validate this observation
CXCR4 Accelerates Osteoclastogenesis Induced by Non-Small Cell Lung Carcinoma Cells Through Self-Potentiation and VCAM1 Secretion
Background/Aims: Non-small cell lung carcinoma (NSCLC) metastasis to bone leads to skeletal-related events and a poor quality of life. Unravelling the mechanism of metastasis is crucial for improving survival. Previous work has implicated the role of CXCR4 in bone metastasis; however, the underlying mechanisms are unknown. Methods: The human bone metastasis tissue samples were obtained from lung cancer patients during surgery with consents. The patients were followed up and the overall survival curve was analysed. The expression of CXCR4, VCAM1, and ADAM17 was measured with real-time PCR, western blot and immunochemistry staining in human tissue or NSCLC cell lines. The effects of CXCR4, soluble VCAM1 and ADAM17 on NSCLC proliferation, migration and invasion were measured with CCK-8, monolayer scratch assay and transwell chamber, respectively. The amount of soluble VCAM1 in the conditioned medium was detected with ELISA. Tartrate-resistant acid phosphatasethe (TRAP) staining was performed to stain the multinucleated cells regarded as osteoclasts. Results: In this study, CXCR4 was found to be highly expressed in bone destruction area of metastatic NSCLC samples and related to poor survival in NSCLC patients with bone metastasis. CXCR4 potentiated NSCLC with enhanced proliferation and invasion abilities, while CXCR4 knockdown significantly suppressed the growth and invasion. Furthermore, CXCR4 promoted lung cancer-induced osteoclast differentiation with increased osteoclast formation. We also found that soluble VCAM1 (sVCAM1) secreted in NSCLC contributed to the osteoclastogenesis induced by CXCR4. The overexpression of CXCR4 increased sVCAM1, and the sVCAM1 secreted from CXCR4-overexpressing NSCLC cells recruited and arrested additional osteoclast progenitors to promote osteoclastogenesis. ADAM17 was confirmed to act as a downstream mediator of CXCR4. The chemical inhibition of ADAM17 with TAPI-2 decreased sVCAM1 secretion and the number of TRAP+ osteoclasts. Conclusion: Taken together, these results indicated that CXCR4 potentiated NSCLC and promoted osteoclastogenesis through sVCAM1, which was cleaved by ADAM17. These data support the pivotal role of the cross talk between CXCR4 and ADAM17-VCAM1 in NSCLC-induced bone metastasis
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Efficient Comparative Sequence Analysis Algorithms for RNA Conserved Structure and Region Detection
RNAs play important roles in multiple cellular processes, and many of their functions rely on folding to specific structures. To maintain their functions, secondary structures of RNA homologs are conserved across evolution. These conserved structures provide critical targets for diagnostics and therapeutics. Thus, there is a need for developing fast and accurate computational methods to identify conserved structural regions. Commonly, conserved structures involve compensating base pair changes, where two changes in the sequence across evolution that conserve a base pair. These compensating changes provide strong evidence for conserved structures. Meanwhile, they also make it harder to align sequences when structures are unknown. To solve this issue, Sankoff proposed a dynamic algorithm that simultaneously predicts structures and an alignment for two or more sequences. Several software packages provide implementations of the Sankoff algorithm. One drawback of this approach is that the Sankoff algorithm runs in O(n3k) against averaged sequence length n and k sequences. TurboFold II, an extension of TurboFold, provides a more computationally efficient method to align and fold sequences. However, it cannot scale to longer sequences due to cubic runtime against the sequence length. We propose LinearTurboFold, which can output the RNA structural alignment and identify conserved base pairs in linear time. LinearTurboFold leads to equal or better accuracies among the benchmarks. With LinearTurboFold, we explored conserved structural regions among betacoronavirus with the well-known RNA structures. Additionally, we listed novel conserved regions whose functions are not well understood. Most existing implementations of the Sankoff algorithm reduce time complexity to O(n3) by restricting the searching space, which still prevents them from being applied to long sequence. Moreover, those algorithms mainly focus on structure prediction and the alignment model is significantly simple, which results in a poor alignment output. We propose LinearSankoff, which extends the classical Sankoff algorithm with a powerful HMM-based alignment model. Based on the evaluation on RNAStrAlign dataset and comparison with benchmark methods, we can conclude that both the alignment and structure prediction accuracies benefit from the HMM-based alignment model. Regarding efficiency, LinearSankoff is a linear version of the classical Sankoff algorithm by applying beam pruning heuristic algorithm. Besides, LinearSankoff applies A∗ algorithm heuristic to accelerate searching, which leads to comparable accuracy with a small beam size
Impact of primary glaucoma on health-related quality of life in China: the handan eye study
Abstract Background We assessed health-related quality of life (HRQOL) and its determinants among rural glaucoma participants compared to age-matched normal controls in the population-based Handan Eye Study (HES), in rural Yongnian County, northern China. Methods We enrolled 99 adults with glaucoma (mean age 63.0 ± 11.0 years), including primary open-angle glaucoma (POAG, n = 67) and primary angle-closure glaucoma (PACG, n = 32) and 102 controls (mean age 58.5 ± 5.3 years) with normal visual acuity and visual field and no history of glaucoma. Results of ophthalmic examinations and socioeconomic data were recorded. HRQOL was measured using the EQ-5D (converted to utility valves, UVs), and visual function (VF) and vision-related quality of life (VRQOL) were evaluated using the visual function-quality of life (VF-QOL) instrument. Primary and secondary outcome measures EQ-5D and VF-QOL scores. Results The mean UVs, VF, and VRQOL scores for glaucoma cases were 0.98 ± 0.04, 87.9 ± 15.2, and 95.5 ± 12.8, respectively, significantly worse than VF (94.4 ± 4.4) and VRQOL (100.0 ± 0.0) among controls, even after adjusting for age, gender, educational level, and family income (P = 0.015, P = 0.033). UVs were significantly lower among glaucoma participants with impaired VRQOL (55.4 ± 11.5) compared to those with normal VRQOL scores (99.1 ± 2.8) (UVs: 0.92 ± 0.08 vs. 0.99 ± 0.03, P = 0.036), also after adjustment for age and family income (P = 0.006). Participants with PACG had significantly lower VF and VRQOL scores compared to POAG (77.8 ± 21.4 vs. 92.9 ± 6.8, P < 0.001; 89.0 ± 18.1 vs. 98.7 ± 7.5, P < 0.001). Conclusion Participants with glaucoma have worse visual function and related quality of life compared to age-matched normal population controls. Participants with PACG have lower VF and VRQOL compared to those with POAG. UVs can be used for cost-effectiveness research and to support public health strategies for glaucoma in rural China