Cognitive decline among older adults with heart diseases before and during the COVID-19 pandemic: A longitudinal cohort study

BackgroundLittle is known about the impact induced by the COVID-19 pandemic on the cognitive function of older adults with heart diseases. This study aimed to examine whether older adults with heart diseases suffered larger cognitive deterioration during the COVID-19 pandemic.MethodsThis study leveraged longitudinal data from the Health and Retirement Study (HRS), a nationally representative U.S. aging cohort with objective cognitive assessments measured before and during the pandemic. The interval from HRS waves 13 to 14 (April 2016 to June 2019) was defined as the pre-pandemic period to control the pre-existed cognitive difference between participants with and without heart diseases, and the interval from waves 14 to 15 (June 2019 to June 2021) was defined as the pandemic period. The HRS wave 14 survey was considered the baseline. The heart disease status was defined by a self-reported diagnosis. Linear mixed models were performed to evaluate and compare the cognitive differences during different periods.ResultsA total of 9,304 participants (women: 5,655, 60.8%; mean age: 65.8 ± 10.8 years) were included, and 2,119 (22.8%) had heart diseases. During the pre-pandemic period, there was no significant difference (−0.03, 95% CI: −0.22 to 0.15, P = 0.716) in the changes in global cognitive scores between participants with and without heart disease. During the pandemic period, a larger decreased change in the global cognitive score was observed in the heart disease group compared with the non-heart disease group (−0.37, 95% CI: −0.55 to −0.19, P < 0.001). An enlarged difference in global cognitive score was observed during the pandemic period (−0.33, 95% CI: −0.65 to −0.02, P = 0.036).ConclusionThe findings demonstrated that the population with heart diseases suffered more cognitive decline related to the pandemic, underscoring the necessity to provide immediate cognitive monitoring and interventions for the population with heart diseases

SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI

Magnetic Resonance Imaging (MRI) is pivotal in radiology, offering non-invasive and high-quality insights into the human body. Precise segmentation of MRIs into different organs and tissues would be highly beneficial since it would allow for a higher level of understanding of the image content and enable important measurements, which are essential for accurate diagnosis and effective treatment planning. Specifically, segmenting bones in MRI would allow for more quantitative assessments of musculoskeletal conditions, while such assessments are largely absent in current radiological practice. The difficulty of bone MRI segmentation is illustrated by the fact that limited algorithms are publicly available for use, and those contained in the literature typically address a specific anatomic area. In our study, we propose a versatile, publicly available deep-learning model for bone segmentation in MRI across multiple standard MRI locations. The proposed model can operate in two modes: fully automated segmentation and prompt-based segmentation. Our contributions include (1) collecting and annotating a new MRI dataset across various MRI protocols, encompassing over 300 annotated volumes and 8485 annotated slices across diverse anatomic regions; (2) investigating several standard network architectures and strategies for automated segmentation; (3) introducing SegmentAnyBone, an innovative foundational model-based approach that extends Segment Anything Model (SAM); (4) comparative analysis of our algorithm and previous approaches; and (5) generalization analysis of our algorithm across different anatomical locations and MRI sequences, as well as an external dataset. We publicly release our model at https://github.com/mazurowski-lab/SegmentAnyBone.Comment: 15 pages, 15 figure

Grid connection method of gravity energy storage generator motor based on voltage index sensitivity analysis

Abstract The basic requirements for the grid connection of the generator motor of the gravity energy storage system are: the phase sequence, frequency, amplitude, and phase of the voltage at the generator end and the grid end must be consistent. However, in actual working conditions, there will always be errors in the voltage indicators of the generator and grid terminals, resulting in transient impulse currents. In addition, due to the difference between gravity energy storage systems and conventional power generation units, frequent switching between charging and discharging operating conditions is required according to the needs of the power grid. Each switching requires the completion of the generator motor startup and grid connection. If there is always a significant error in the voltage indicators between the generator and grid terminals during frequent grid connection, stable transient surge currents will be generated. Without human intervention, long-term operation will bring hidden dangers to the safety of the grid connected system, leading to a series of consequences such as equipment aging and even damage. In response to the above issues, this article establishes a gravity energy storage power generation/motor grid connection model. Through simulation analysis, the variation law of the weight of the impact of different terminal voltage indicators on the grid connected transient impulse current is summarized. A grid connection method for gravity energy storage systems based on sensitivity analysis of voltage grid connection indicators is proposed. Through simulation verification, this method can significantly reduce the grid connected transient impulse current while improving the success rate of grid connection, The correctness and practicality of the proposed method have been fully verified

A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability

To accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. The prediction variance ratio test method is introduced to determine the optimal dimension of the VSVs. The VSV-BDA model is constructed through the use of VSVs and the optimal dimension on the basis of the training samples, which are divided from the collected samples into three situations having different numbers. ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering

Association Between Onset Age of Coronary Heart Disease and Incident Dementia: A Prospective Cohort Study

Background The association of age at coronary heart disease (CHD) onset with incident dementia remains unexplored. This study aimed to examine whether younger onset age of CHD is associated with a higher risk of incident dementia. Methods and Results Data were obtained from the UK Biobank. Information on the diagnosis of CHD and dementia was collected at baseline and follow‐ups. Propensity score matching method and Cox proportional hazards models were used to evaluate the association between different ages at CHD onset and incident dementia. A total of 432 667 adults (mean±SD age, 56.9±8.1 years) were included, of whom 11.7% had CHD. Compared with participants without CHD, participants with CHD exhibited higher risks of developing all‐cause dementia, Alzheimer's disease, and vascular dementia. More importantly, younger age at CHD onset (per 10‐year decrease) was significantly associated with elevated risks of all‐cause dementia (hazard ratio [HR], 1.25 [95% CI, 1.20–1.30]; P<0.001), Alzheimer's disease (HR, 1.29 [95% CI, 1.20–1.38]; P<0.001), and vascular dementia (HR, 1.22 [95% CI, 1.13–1.31]; P<0.001). After propensity score matching, patients with CHD had significantly higher risks of all‐cause dementia, Alzheimer's disease, and vascular dementia than matched controls among all onset age groups, and the HRs gradually elevated with decreasing age at CHD onset. Conclusions Younger onset age of CHD is associated with higher risks of incident all‐cause dementia, Alzheimer's disease, and vascular dementia, underscoring the necessity to pay attention to the neurocognitive status of individuals diagnosed with CHD at younger age to conduct timely interventions to attenuate subsequent risk of incident dementia

A review of underwater inductive wireless power transfer system

Abstract The IPT system has been studied for underwater applications such as autonomous underwater vehicles (AUVs) and subsea sensors. However, it rarely comparatively shows the performance of the IPT system in air, freshwater, and seawater. Based on the fore‐mentioned research background, this paper presents a survey of the properties of the IPT system in different mediums. Here, a 100 W power‐level experimental IPT prototype is built and tested. The resonant frequency is set at 300 kHz with a gap range from 10 to 190 mm. The comparison is focused on the efficiency, mutual inductance, coupling coefficient, coil resistance, and quality factor of the IPT system. The IPT system is placed in air, freshwater, and seawater with the same settings. What's more, the magnetic fields of coupling coils in air, freshwater, and seawater are presented in this paper. This paper could be acted as a reference to optimize the IPT system and facilitate future IPT research for underwater applications by analysing the performance of the IPT system in different mediums. The 3D Ansys Maxwell simulation of the IPT system is also given here to study the magnetic fields

Ligand-Directed Regioselectivity in Amine–Imine Nickel-Catalyzed 1‑Hexene Polymerization

1-Hexene polymerizations were carried out with amine–imine nickel complexes [(ArNC­(R¹)–(R²R³)­CNHAr)­NiBr₂, <b>1a</b>, R¹ = R² = R³ = Me, Ar = 2,6-(iPr)₂C₆H₃; <b>1b</b>, R¹ = R² = R³ = Me, Ar = 2,6-(Me)₂C₆H₃; <b>2a</b>, R¹ = Me, R² = R³ = H, Ar = 2,6-(iPr)₂C₆H₃; <b>3a</b>, R¹ = Me, R² = <i>t</i>Bu, R³ = H, Ar = 2,6-(iPr)₂C₆H₃] in the presence of MMAO or Et₂AlCl. The ligand-directed regioselectivity involving insertion fashion and chain walking in amine–imine nickel-catalyzed 1-hexene polymerization is clearly observed. Catalyst <b>1a</b> with two methyl substituents on the bridging carbon can polymerize 1-hexene to afford semicrystalline “polyethylene” with long methylene sequence (−(CH₂)<i>_n</i>–, <i>n</i> = 40–74) via a combination of 90% selectivity of 2,1-insertion fashion and precise chain walking, whereas catalyst <b>3a</b> with a <i>tert</i>-butyl on the bridging carbon can polymerize 1-hexene in 80% selectivity of 1,2-insertion to produce amorphous polyolefin with predominant methyl branches through 2,6-enchainment