Learning From Peers: A Survey of Perception and Utilization of Online Peer Support Among Informal Dementia Caregivers

Informal dementia caregivers are those who care for a person living with dementia (PLWD) without receiving payment (e.g., family members, friends, or other unpaid caregivers). These informal caregivers are subject to substantial mental, physical, and financial burdens. Online communities enable these caregivers to exchange caregiving strategies and communicate experiences with other caregivers whom they generally do not know in real life. Research has demonstrated the benefits of peer support in online communities, but they are limited in focusing merely on caregivers who are already online users. In this paper, we designed and administered a survey to investigate the perception and utilization of online peer support from 140 informal dementia caregivers (with 100 online-community caregivers). Our findings show that the behavior to access any online community is only significantly associated with their belief in the value of online peer support (p = 0.006). Moreover, 33 (83%) of the 40 non-online-community caregivers had a belief score above 24, a score assigned when a neutral option is selected for each belief question. The reasons most articulated for not accessing any online community were no time to do so (14; 10%), and insufficient online information searching skills (9; 6%). Our findings suggest that online peer support is valuable, but practical strategies are needed to assist informal dementia caregivers who have limited time or searching skills

Using AKF-PSR to Compensate Random Drift Errors of Low-Cost MEMS Gyroscopes

The random drift of a micro-electromechanical system (MEMS) gyroscope seriously affects its measurement accuracy. To model and compensate its random drift, the time series analysis method has widely been deployed, which, however, requires a large amount of data for pre-processing analysis and is unsuitable for real-time applications. This paper proposes a new random drift compensation method based on the adaptive Kalman filter (AKF) and phase space reconstruction (PSR). AKF is first designed to compensate the random drift of the low-cost MEMS gyroscope. The phase variables are then used as phase vectors via PSR. Experiments show that the proposed AKF-PSR method can effectively compensate the random drift of the gyroscope, and the standard deviation is reduced by half

The Airlines’ Recent Experience Under the Railway Labor Act

Silky-feather has been selected and fixed in some breeds due to its unique appearance. This phenotype is caused by a single recessive gene (hookless, h). Here we map the silky-feather locus to chromosome 3 by linkage analysis and subsequently fine-map it to an 18.9 kb interval using the identical by descent (IBD) method. Further analysis reveals that a C to G transversion located upstream of the prenyl (decaprenyl) diphosphate synthase, subunit 2 (PDSS2) gene is causing silky-feather. All silky-feather birds are homozygous for the G allele. The silky-feather mutation significantly decreases the expression of PDSS2 during feather development in vivo. Consistent with the regulatory effect, the C to G transversion is shown to remarkably reduce PDSS2 promoter activity in vitro. We report a new example of feather structure variation associated with a spontaneous mutation and provide new insight into the PDSS2 function

Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD

Comparison of 2D and 3D prediction models for environmental vibration induced by underground railway with two types of tracks

Two-dimensional (2D) and three-dimensional (3D) prediction models for environmental vibration induced by underground railway with direct fixation track and steel spring floating slab track are developed and verified. The responses of ground surface calculated by 2D prediction models with various equivalent forces are compared to those calculated by 3D prediction models. The numerical results show that (a) the computational time for each case calculated by 2D prediction models is more than 500 times less than that calculated by 3D prediction models, however, the accuracy of 2D prediction models is relatively lower than 3D prediction models, so 3D prediction models are required for absolute prediction due to their higher accuracy and applicability to a wider range of complex problems; and (b) a suitable equivalent force transfer method for 2D prediction models can improve the prediction accuracy of 2D prediction models, the equivalent forces in 2D prediction models are respectively recommended to use the equivalent wheel-rail force and the equivalent steel spring force averaged over a vehicle length for underground direct fixation track and steel spring floating slab trac

The Crest Phenotype in Chicken Is Associated with Ectopic Expression of HOXC8 in Cranial Skin

The Crest phenotype is characterised by a tuft of elongated feathers atop the head. A similar phenotype is also seen in several wild bird species. Crest shows an autosomal incompletely dominant mode of inheritance and is associated with cerebral hernia. Here we show, using linkage analysis and genome-wide association, that Crest is located on the E22C19W28 linkage group and that it shows complete association to the HOXC-cluster on this chromosome. Expression analysis of tissues from Crested and non-crested chickens, representing 26 different breeds, revealed that HOXC8, but not HOXC12 or HOXC13, showed ectopic expression in cranial skin during embryonic development. We propose that Crest is caused by a cis-acting regulatory mutation underlying the ectopic expression of HOXC8. However, the identification of the causative mutation(s) has to await until a method becomes available for assembling this chromosomal region. Crest is unfortunately located in a genomic region that has so far defied all attempts to establish a contiguous sequence

Uncovering the Role of Fat-Infiltrated Axillary Lymph Nodes in Obesity-Related Diseases with Statistical and Machine Learning Analyses

The link between obesity and pathogenesis is a complex and multifaceted area of research that is yet to be fully understood. Ample evidence exists to demonstrate the direct relationship between excessive internal fat and various health conditions such as cancer, and metabolic and cardiovascular diseases. The infiltration of ectopic fat into axillary lymph nodes, observable on breast cancer screening images, has been shown to be correlated with body mass index (BMI) in women undergoing screening. This study aimed to explore the relationship between fat-infiltrated axillary lymph nodes (FIN) and obesity-related diseases, with the goal of evaluating the clinical value of FIN as a potential prognostic image biomarker. In this thesis, we conducted a comprehensive examination of the potential association between FIN and breast cancer nodal metastasis in breast cancer patients with obesity. We also investigated the relationship between FIN and multiple cardiometabolic diseases in women who have received screening mammography. Additionally, we developed a deep-learning pipeline to automate the detection of FIN from screening mammography, with the aim of facilitating large-scale studies in the future. Lastly, by leveraging the deep-learning model, we identified potential morphological correlates of lymph node adiposity on the histology images, which helped to generate hypotheses for the underlying mechanisms that drive the FIN-obesity pathogenesis. Overall, this thesis underscores the significance of FIN in the development of obesity-related diseases and establishes a foundation for future research to gain a deeper understanding of the complex relationship between FIN and obesity-related diseases