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.
AbstractBackgroundThere is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which ...might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain. MethodsA multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy ( n = 110) or diagnosed with bipolar disorder ( n = 40), attention-deficit/hyperactivity disorder ( n = 37), schizophrenia ( n = 29), or schizoaffective disorder ( n = 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components. ResultsOur analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network and its connectivity with subcortical structures and cortical executive networks. ConclusionsWe identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span across diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.
Heterogeneity in autism spectrum disorder (ASD) has hindered the development of biomarkers, thus motivating subtyping efforts. Most subtyping studies divide individuals with ASD into nonoverlapping ...(categorical) subgroups. However, continuous interindividual variation in ASD suggests that there is a need for a dimensional approach.
A Bayesian model was employed to decompose resting-state functional connectivity (RSFC) of individuals with ASD into multiple abnormal RSFC patterns, i.e., categorical subtypes, henceforth referred to as “factors.” Importantly, the model allowed each individual to express one or more factors to varying degrees (dimensional subtyping). The model was applied to 306 individuals with ASD (5.2–57 years of age) from two multisite repositories. Post hoc analyses associated factors with symptoms and demographics.
Analyses yielded three factors with dissociable whole-brain hypo- and hyper–RSFC patterns. Most participants expressed multiple (categorical) factors, suggestive of a mosaic of subtypes within individuals. All factors shared abnormal RSFC involving the default mode network, but the directionality (hypo- or hyper–RSFC) differed across factors. Factor 1 was associated with core ASD symptoms. Factors 1 and 2 were associated with distinct comorbid symptoms. Older male participants preferentially expressed factor 3. Factors were robust across control analyses and were not associated with IQ or head motion.
There exist at least three ASD factors with dissociable whole-brain RSFC patterns, behaviors, and demographics. Heterogeneous default mode network hypo- and hyper–RSFC across the factors might explain previously reported inconsistencies. The factors differentiated between core ASD and comorbid symptoms—a less appreciated domain of heterogeneity in ASD. These factors are coexpressed in individuals with ASD with different degrees, thus reconciling categorical and dimensional perspectives of ASD heterogeneity.
•The phase locking behavior of superparamagnetic tunnel junctions (STJs) as stochastic magnetic bits induced by colored noise is studied.•White, blue and violet noise can promote the subthreshold STJ ...synchronization.•The phase locking of STJs is suppressed by pink noise and red noise.•Blue noise can make STJs get the lowest phase locking power consumption with order of 10−13J.
Superparamagnetic tunnel junctions (STJs) are nanostructures with very low turnover barriers. The barrier height of an STJ is generally equal to the heat energy at room temperature; thus, it can oscillate automatically without external driving. Previous studies have shown that the randomness of an STJ can be driven by a subthreshold voltage. This synchronization can be adjusted using electrical noise, which is often considered as zero-field Gaussian white noise. However, the actual circuit and environment are inevitably associated with colored noise, which has not been considered previously. In this work, numerical simulations were performed to study the phase-locking characteristics of a single STJ with the aid of several typical types of colored noise. The results show that the phase-locked behavior of an STJ can be effectively enhanced by colored noise whose power spectral density per unit of bandwidth is proportional to its frequency. Meanwhile, colored noise whose power spectral density per unit of bandwidth and frequency are inversely proportional can suppress the synchronization of STJs by suppressing the increase in junction frequency.
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/Y
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bilayer structure. We demonstrated that the CoFeB/Y
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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
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mT order can be experimentally achieved. Furthermore, the higher sensitivity in the order of 10
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bilayer device. A Y-shaped spin waves interference device with two input arms consisting of CoFeB/Y
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and Y
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has been theoretically investigated. We proposed that such a structure can demonstrate a magnetic sensitivity in the range of
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T (nT) at room temperature. The sensitivity of the sensor can be further enhanced by tuning the width of the CoFeB metal stripe.
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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 (
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’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis.
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’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.
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.
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.
, 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.
This paper focuses on the preparation of environmentally friendly biomass carbon-based solid acid catalysts from black soldier fly larvae (BSFL) shells. The effective catalysts were prepared by ...incomplete carbonization of BSFL shells and then the p-toluenesulfonic acid (PTSA) groups were introduced to obtain catalysts. The synthesized composite catalysts were characterized by XPS, FT-IR, SEM, XRD, and BET analysis to reveal the physiochemical properties. Meanwhile, the effects of various reaction conditions on the yield of biodiesel with oleic acid (OA) and methanol were studied in detail. The results showed that the conversion rate reached 93.2% under the optimal conditions in this paper: carbonization temperature = 400 °C, carbonization time = 2 h, sulfonation temperature = 100 °C, and reaction time = 4 h, respectively. Furthermore, the stability and reusability of the prepared catalysts were also demonstrated. At last, the possible catalytic mechanism of the prepared catalyst was comprehensively described. Moreover, the results showed that the synthesized biomass-based solid acid catalyst under these conditions had good catalytic activity.
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