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Abstract Gain‐dissipative Ising machines (GIMs) are annealers inspired by physical systems such as Ising spin glasses to solve combinatorial optimization problems. Compared to traditional quantum annealers, GIM is relatively easier to scale and can save on additional power consumption caused by low‐temperature cooling. However, traditional GIMs have a limited noise margin. Specifically, their normal operation requires ensuring that the noise intensity is lower than their saturation fixed point amplitude, which may result in increased power consumption to suppress noise‐induced spin state switching. To enhance the noise robustness of GIM, in this study a GIM based on a topologically defective lattice potential (TDLP) is proposed. Numerical simulations demonstrate that the TDLP‐based GIM can accurately simulate the bifurcation spin evolution in the Ising model. Furthermore, through the MAXCUT benchmark based on G‐set graphs, the optimal performance of TDLP‐based GIM is shown to surpass that of traditional GIMs. Additionally, the proposed TDLP‐based GIM successfully solves the MAXCUT benchmark and domain clustering dynamics benchmark based on G‐set graphs when the noise intensity exceeds its saturation fixed‐point amplitude. This indicates that the proposed system provides a promising architecture for breaking the small noise constraints required by traditional GIMs.

Abstract A major barrier to deploying healthcare AI is trustworthiness. One form of trustworthiness is a model’s robustness across subgroups: while models may exhibit expert-level performance on aggregate metrics, they often rely on non-causal features, leading to errors in hidden subgroups. To take a step closer towards trustworthy seizure onset detection from EEG, we propose to leverage annotations that are produced by healthcare personnel in routine clinical workflows—which we refer to as workflow notes—that include multiple event descriptions beyond seizures. Using workflow notes, we first show that by scaling training data to 68,920 EEG hours, seizure onset detection performance significantly improves by 12.3 AUROC (Area Under the Receiver Operating Characteristic) points compared to relying on smaller training sets with gold-standard labels. Second, we reveal that our binary seizure onset detection model underperforms on clinically relevant subgroups (e.g., up to a margin of 6.5 AUROC points between pediatrics and adults), while having significantly higher FPRs (False Positive Rates) on EEG clips showing non-epileptiform abnormalities (+19 FPR points). To improve model robustness to hidden subgroups, we train a multilabel model that classifies 26 attributes other than seizures (e.g., spikes and movement artifacts) and significantly improve overall performance (+5.9 AUROC points) while greatly improving performance among subgroups (up to +8.3 AUROC points) and decreasing false positives on non-epileptiform abnormalities (by 8 FPR points). Finally, we find that our multilabel model improves clinical utility (false positives per 24 EEG hours) by a factor of 2×.

Rosacea is a chronic inflammatory dermatosis that involves dysregulation of innate and adaptive immune systems. Osteopontin (OPN) is a phosphorylated glycoprotein produced by a broad range of immune cells such as macrophages, keratinocytes, and T cells. However, the role of OPN in rosacea remains to be elucidated. In this study, it was found that OPN expression was significantly upregulated in rosacea patients and LL37-induced rosacea-like skin inflammation. Transcriptome sequencing results indicated that OPN regulated pro-inflammatory cytokines and promoted macrophage polarization towards M1 phenotype in rosacea-like skin inflammation. In vitro, it was demonstrated that intracellular OPN (iOPN) promoted LL37-induced IL1B production through ERK1/2 and JNK pathways in keratinocytes. Moreover, secreted OPN (sOPN) played an important role in keratinocyte-macrophage crosstalk. In conclusion, sOPN and iOPN were identified as key regulators of the innate immune system and played different roles in the pathogenesis of rosacea.

Abstract Spin waves (SWs), an ultra-low power magnetic excitation in ferro or antiferromagnetic media, have tremendous potential as transport less data carriers for post-CMOS technology using their wave interference properties. The concept of magnon interference originates from optical interference, resulting in a historical taboo of maintaining an identical wavevector for magnon interference-based devices. This makes the attainment of on-chip design reconfigurability challenging owing to the difficulty in phase tuning via external fields. Breaking the taboo, this study explores a novel technique to systematically control magnon interference using asymmetric wavevectors from two different SW modes (magnetostatic surface SWs and backward volume magnetostatic SWs) in a microstructured yttrium iron garnet crossbar. Using this system, we demonstrate phase reconfigurability in the interference pattern by modulating the thermal landscape, modifying the dispersion of the interfering SW modes. Thus, we manifest that such a tunable interference can be used to implement reconfigurable logic gates operating between the XNOR and XOR modes by using symmetric and asymmetric interference, respectively.

This research aimed to study the preventive and relieving outcomes of kiwi berry on constipation. The administration of kiwi berries to mice resulted in a significant increase in body weight gain of 148.2% compared to mice that were constipated. The number of stools and the water content of stools both increased by 138.5% and 106.5%, respectively. The gastrointestinal transit rate increased by 45.3%, and the time it took for the first dark stool to form decreased by 57.5%. The levels of the excitability neurotransmitters were found to be higher in the group that had been given kiwi berries in comparison to the group that had been given loperamide. The opposite results were produced by vasoactive intestinal peptide (VIP) and aquaporin-3 (AQP3). In addition, kiwi berry consumption may lessen epithelial cell apoptosis and promote colon health. All the results point to kiwi berries as an extremely promising food supplement for the prevention and relief of constipation in the future since they successfully prevent and alleviate constipation brought on by loperamide.

Logical stochastic resonance (LSR) system is a physical system capable of performing robust reconfigurable logical operations in the presence of background noise using specific nonlinearities. Traditional LSR systems are typically based on polynomial nonlinearities, which make them suitable for implementation by electronic components. However, there has been little research on LSR systems based on hyperbolic nonlinearities which have the potential to be realized directly using the physical properties of materials. Inspired by the kink-bearing potential related to phonon nonlinear excitation, we propose an LSR system based on a hyperbolic one-site lattice (HOL) for the first time in this study. The performance of the HOL-based LSR (HOL-LSR) system is investigated under both noise-free and noisy conditions. Moreover, assisted by a bionic optimizer, we compare the performance of the proposed HOL-LSR and traditional quadstable LSR (QLSR) system under the influence of Gaussian white noise and Lévy pulse. The results demonstrate the existence of parameter-induced LSR effects in the HOL-LSR system. In addition, with only Gaussian white noise, the performance of the HOL-LSR and QLSR systems is comparable. However, when the background noise contains pulse components, the robustness of the HOL-LSR system is significantly stronger than that of the QLSR system. These results prove the superiority of HOL-LSR system over traditional LSR and encourage the construction of LSR systems directly based on material with hyperbolic property.

Abstract Magnonics, an emerging research field that uses the quanta of spin waves as data carriers, has a potential to dominate the post-CMOS era owing to its intrinsic property of ultra-low power operation. Spin waves can be manipulated by a wide range of parameters; thus, they are suitable for sensing applications in a wide range of physical fields. In this study, we designed a highly sensitive, simple structure, and ultra-low power magnetic sensor using a simple CoFeB/Y3Fe5O12 bilayer structure. We demonstrated that the CoFeB/Y3Fe5O12 bilayer structure can create a sharp rejection band in its spin-wave transmission spectra. The lowest point of this strong rejection band allows the detection of a small frequency shift owing to the external magnetic field variation. Experimental observations revealed that such a bilayer magnetic sensor exhibits 20 MHz frequency shifts upon the application of an external magnetic field of 0.5 mT. Considering the lowest full width half maximum, which is about 2 MHz, a sensitivity of 10–2 mT order can be experimentally achieved. Furthermore, the higher sensitivity in the order of 10–6 T (µT) has been demonstrated using the sharp edge of the rejection band of the CoFeB/Y3Fe5O12 bilayer device. A Y-shaped spin waves interference device with two input arms consisting of CoFeB/Y3Fe5O12 and Y3Fe5O12 has been theoretically investigated. We proposed that such a structure can demonstrate a magnetic sensitivity in the range of $${10}^{-9}$$ 10 - 9 T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe.

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ( $${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

Abstract The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors. Therefore, algorithms that can automatically identify low quality data are highly desired. In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset. We characterized the effectiveness of data Shapley in identifying low quality versus valuable data for pneumonia detection. We found that removing training data with high Shapley values decreased the pneumonia detection performance, whereas removing data with low Shapley values improved the model performance. Furthermore, there were more mislabeled examples in low Shapley value data and more true pneumonia cases in high Shapley value data. Our results suggest that low Shapley value indicates mislabeled or poor quality images, whereas high Shapley value indicates data that are valuable for pneumonia detection. Our method can serve as a framework for using data Shapley to denoise large-scale medical imaging datasets.

Superparamagnetic tunnel junctions (SMTJs) are spintronic nanodevices that can oscillate spontaneously under the influence of thermal noise. Studies have proposed that SMTJs can be modeled as magnetic neurons by subjecting them to electrical Gaussian white noise and can be subsequently synchronized under subthreshold driving. Considering the non-Gaussian background noise in industrial applications, this study investigated the influence of levy noise on SMTJ subthreshold synchronization. The results revealed that levy noise with weak pulse characteristics can be used to promote the subthreshold synchronization of the SMTJs, whereas levy noise with enhanced pulse characteristics cannot be used. Under the same conditions, enhanced levy noise asymmetry is advantageous for achieving SMTJ synchronization under subthreshold driving.

The study was to explore the effect of the extracts of Angelica sinensis (EAs) on lipid oxidation in fish feeds compared with ethoxyquin (EQ) and the effect of dietary EAs on growth performance of carp (Cyprinus carpio var. Jian). Firstly, fish feeds were respectively added with EQ, and ethyl ether extract, ethyl acetate extract (EAE), acetone extract, ethanol extract (EE) and aqueous extract (AQE) of Angelica sinensis, except for the control. The results showed that EAs and EQ inhibited lipid oxidation in fish feeds (P < 0.05). Of all of the examined EAs, EAE showed the strongest protective effects against lipid oxidation (P < 0.05). Moreover, EAE at high concentrations showed a stronger effect on lipid oxidation compared with EQ (P < 0.05). Then, 7 experimental diets respectively supplemented with 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 and 6.0 g/kg of EAE were fed to the respective treatment groups for 30 d. Four replicates were performed for each treatment group; 20 carp (mean weight: 12.10 ± 0.13 g) were in each replicate. The results indicated that dietary EAE improved the growth performance in carp (P < 0.05). The appropriate concentration of EAE for carp growth was estimated to be 3.643 g/kg diet. Thus, EAE could be used as a natural antioxidant in feeds for Jian carp. Keywords: Angelica sinensis, Extracts, Ethoxyquin, Lipid oxidation, Growth performance

In this study, a series of acid-base bi-functional catalysts were prepared by mixing different amounts of basic amino acids (AAs) and phosphotungstic acid (PTA), which exhibited the excellent performance in catalyzing conversion of oleic acid (OA) for the biodiesel production. The physicochemical properties of the catalysts were characterized and analyzed using modern testing techniques and characterization methods such as XRD, FT-IR, XPS, SEM, TEM, and Hammett titration. The various influence parameters were optimized using the central composite design based the response surface methodology, where the maximum biodiesel yield of 97.0% was achieved at the MeOH/OA molar ratio of 5.9, the catalyst loading of 8%, reaction time of 6 h, and reaction temperature of 65°C. Furthermore, the stability and reusability of the prepared catalyst were also demonstrated. At last, the possible catalytic mechanism of the prepared catalyst was comprehensively described. [ABSTRACT FROM AUTHOR]