异氟烷对幼小鼠自主活动和学习记忆行为的影响

目的观察异氟烷(Iso)对幼小鼠自主活动和学习记忆行为的影响,探讨其与γ-氨基丁酸A受体(GABAA)的关系。方法 360只小鼠分为3大组,分别进行自主活动实验、避暗实验和跳台实验,每大组按分层随机区组设计分为正常对照组,Iso 0.05,0.1和0.2 ml·kg-1组、GABAA受体特异性阻断剂一叶萩碱(Sec)2,4和8 mg.kg-1组和Iso 0.2 m.lkg-1+Sec 2,4和8 mg.kg-1组。小鼠sc给予Sec 10 min后,再ip给予Iso,测试给药后15,30,45和60 min小鼠5 min内活动次数;跳台仪和避暗仪记录小鼠步入、跳下的潜伏期和错误次数。结果与同一时间点的正常对照组相比,Iso可减少小鼠自主活动次数,给予Iso后1 d小鼠避暗实验和跳台实验的步入和跳下潜伏期缩短以及错误次数增加(P<0.05)。正常小鼠单独给Sec(除Sec 8 mg·kg-1组15 min外)对小鼠自主活动、步入和跳下潜伏期和错误次数无明显影响,但Sec可改善Iso导致的小鼠自主活动次数,拮抗Iso对小鼠自主活动的影响,但随着时间延长拮抗作用逐渐减弱,明显低于正常对照组(P<0.05)。Sec延长Iso小鼠的跳下和步入潜伏期,减少错误次数,基本恢复至正常对照组水平,明显改善Iso对幼小鼠学习记忆的影响(P<0.05)。结论 Iso可降低幼小鼠自主活动次数,损害学习记忆能力,GABAA受体可能部分参与了以上作用。     

基于深度学习的超声影像诊断对终末期慢性肾病的价值

目的 探讨基于深度学习的卷积神经网络模型DenseNet121对终末期肾病的诊断价值.方法 回顾性选择2019年1月1日至9月30日期间中日友好医院诊断为终末期肾病的489张肾超声影像和对照组450张健康肾超声影像.采用深度卷积神经网络模型DenseNet121进行网络的训练和验证,把是否为终末期肾病作为参考标准.然后...     

Study on Electrical Pitting Prevention Device of a Rotating Shaft Using Automatic Control Potential Balancing

A rotating body consisting of a rotating shaft and bearings inevitably generates voltage and current. The potential difference between the bearing and the shaft is the main cause of electrical corrosion, which causes motor failure, shortened bearing life, and many safety issues. To prevent corrosion, passive shaft-grounding devices use conductive materials and brushes; however, these devices cannot be completely grounded, so there is a difference in local potential, and brush friction generates a shaft current. The cumulative effect causes electrical corrosion; therefore, in this study, an electrical corrosion protection device for the rotating power supply shaft was developed. It detected current and potential difference and established a feedback system on the rotating shaft. It also energized the rotating shaft using an external power supply to eliminate the potential difference on the shaft and reduce electrical corrosion. The result was prolonged motor life and improved stability, operating efficiency, and operability of related equipment. In this study, a rotating-shaft test rig was set up, and a constant current was applied to simulate the potential difference and verify the performance of the anti-corrosion device. Gradually, the design scheme was optimized; the potential difference on the rotating shaft was accurately quantified; and the goal of controlling the potential difference within 2 mV was achieved. Finally, the electrical corrosion protection device was applied to the rotating shaft of a merchant ship, and the current and potential difference on the rotating shaft were monitored for 30 days. The results showed that the device had excellent performance in reducing the potential difference on the rotating shaft and preventing electrical corrosion.     

dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling

ObjectiveTo develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling.Materials and MethodsThe GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual patient data (IPD) privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed penalized quasi-likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than IPD, can be shared across hospitals. We also show that the standardized mortality rates, which are often reported as the results of hospital profiling, can also be calculated distributively without sharing IPD. We demonstrate the applicability of the proposed dPQL algorithm by ranking 929 hospitals for coronavirus disease 2019 (COVID-19) mortality or referral to hospice that have been previously studied.ResultsThe proposed dPQL algorithm is mathematically proven to be lossless, that is, it obtains identical results as if IPD were pooled from all hospitals. In the example of hospital profiling regarding COVID-19 mortality, the dPQL algorithm reached convergence with only 5 iterations, and the estimation of fixed effects, random effects, and mortality rates were identical to that of the PQL from pooled data.ConclusionThe dPQL algorithm is lossless, privacy-preserving and fast-converging for fitting GLMM. It provides an extremely suitable and convenient distributed approach for hospital profiling.     

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations.A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results.Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets.A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.     

The sexual brain,genes, and cognition: A machine‐predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9–10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC–AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (p _fdr < .001, ηp2 = .011–.034; adjusted for covariates) and higher cognitive genome‐wide polygenic scores (GPSs) (p _fdr < .001, ηp2 < .005). Structural equation models revealed that the GPS‐intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006–.009; p = .002–.022; sex‐stratified analysis). The finding of the sex modulatory effect on the gene–brain–cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.     

Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient’s viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.     

Learning under social versus nonsocial uncertainty: A meta‐analytic approach

Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta‐analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action.     

Decoupling location specificity from perceptual learning of orientation discrimination

Perceptual learning of orientation discrimination is reported to be precisely specific to the trained retinal location. This specificity is often taken as evidence for localizing the site of orientation learning to retinotopic cortical areas V1/V2. However, the extant physiological evidence for training improved orientation turning in V1/V2 neurons is controversial and weak. Here we demonstrate substantial transfer of orientation learning across retinal locations, either from the fovea to the periphery or amongst peripheral locations. Most importantly, we found that a brief pretest at a peripheral location before foveal training enabled complete transfer of learning, so that additional practice at that peripheral location resulted in no further improvement. These results indicate that location specificity in orientation learning depends on the particular training procedures, and is not necessarily a genuine property of orientation learning. We suggest that non-retinotopic high brain areas may be responsible for orientation learning, consistent with the extant neurophysiological data.