Miracle-dz/UniFAR-FTUs huggingface dataset:
Individual variation in preference behavior in sailfin fish refines the neurotranscriptomic pathway for mate preference

Abstract Social interactions can drive distinct gene expression profiles which may vary by social context. Here we use female sailfin molly fish (Poecilia latipinna) to identify genomic profiles associated with preference behavior in distinct social contexts: male interactions (mate choice) versus female interactions (shoaling partner preference). We measured the behavior of 15 females interacting in a non‐contact environment with either two males or two females for 30 min followed by whole‐brain transcriptomic profiling by RNA sequencing. We profiled females that exhibited high levels of social affiliation and great variation in preference behavior to identify an order of magnitude more differentially expressed genes associated with behavioral variation than by differences in social context. Using a linear model (limma), we took advantage of the individual variation in preference behavior to identify unique gene sets that exhibited distinct correlational patterns of expression with preference behavior in each social context. By combining limma and weighted gene co‐expression network analyses (WGCNA) approaches we identified a refined set of 401 genes robustly associated with mate preference that is independent of shoaling partner preference or general social affiliation. While our refined gene set confirmed neural plasticity pathways involvement in moderating female preference behavior, we also identified a significant proportion of discovered that our preference‐associated genes were enriched for ‘immune system’ gene ontology categories. We hypothesize that the association between mate preference and transcriptomic immune function is driven by the less well‐known role of these genes in neural plasticity which is likely involved in higher‐order learning and processing during mate choice decisions.
A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer’s Disease Diagnosis

Objective: Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD. Methods: We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of <inline-formula> <tex-math notation="LaTeX">$\text{A}\beta +$ </tex-math></inline-formula> AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy. Results: We tested the UNB-GCN framework on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal <inline-formula> <tex-math notation="LaTeX">$\text{A}\beta +$ </tex-math></inline-formula> AD, <inline-formula> <tex-math notation="LaTeX">$\text{A}\beta +$ </tex-math></inline-formula> mild cognitive impairment (MCI) and <inline-formula> <tex-math notation="LaTeX">$\text{A}\beta +$ </tex-math></inline-formula> cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set. Conclusion: The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.
Smoking relapse reasons among current smokers with previous cessation experience in Shanghai: A cross-sectional study

INTRODUCTION Quitting smoking can lead to substantial health gains, even later in life. Many smokers who attempt to quit experience several relapses before achieving sustainable cessation. This study aims to ascertain the differences between quitters with short and long abstinence time and to explore relapse reasons among smokers with cessation experience in Shanghai. METHODS From January to December 2022, 1745 current smokers were recruited in Minhang, Jiading, Qingpu and Songjiang districts of Shanghai. We used an electronic questionnaire to collect data. We implemented logistic regression for odds ratio (OR) and 95% confidence interval (CI) calculation to explore factors associated with long cessation time among smokers with cessation experience of ≥3 months, ≥6 months, and ≥12 months. RESULTS Of the 1745 smokers included, 1452 (83.2%) were males, with an average age of 44.2 years, and 48.0% (838/1745) had cessation experience but relapsed. Logistic regression indicated that smokers aged ≥45 years had a longer cessation duration (adjusted odds ratio, AOR=3.10; 95% CI: 1.97–4.88). Moreover, longer cessation duration among smokers was positively associated with low education level of junior high or lower (AOR=2.30; 95% CI: 1.42–3.72) and senior high (AOR=2.19; 95% CI: 1.53–3.15), older age at first tobacco smoking (AOR=1.62; 95% CI: 1.1.16–2.25), but was negatively associated with longer smoking duration (AOR=0.67; 95% CI: 0.43–0.00) and higher smoking burden (AOR=0.44; 95% CI: 0.28–0.72). The main reasons for cessation relapse were social interaction needs (34.5%) and discomfort due to abstinence (29.1%). CONCLUSIONS The relapse rate was high among smokers even after 12 months of abstinence. Smokers with older age, lower education level, shorter smoking duration and lower tobacco burden had longer cessation duration. Social interaction needs and withdrawal symptoms were the main relapse reasons. It is highly recommended that health bureaux consistently conduct tobacco control initiatives to spread awareness about the detrimental effects of tobacco smoke and the advantages of quitting smoking, even after achieving cessation.
Outlier detection based on Linear programming

Outlier detection is an important step in many data-mining applications. In this paper, we propose an outlier detection mathod based on Linear Programming. The essential idea behind this technique is that two neighbor data points must be normal points or outliers in the same time, this is consistent with Markov property, hence we construct k-nearest neighbor graph model. As the main result of this paper, we show that Linear Programming method can detect outliers correctly, even if the data has outliers that form a small cluster, in contrast to state of the art outlier detection algorithm LOF.
Optical nanoscopy with contact Mie-particles: Resolution analysis

The theoretical limits of resolution available in microspherical nanoscopy are explored using incoherent point emitters in the air. The images are calculated using a two-dimensional model and solving the Maxwell equations which account for the wave effects on the sub-wavelength scale of the emitter-microsphere interaction. Based on our results, we propose to use small dielectric particles with diameters λ ≲ D ≲ 2λ made of a high-refractive-index material n∼2 for imaging sub-wavelength objects. It is shown that such particles form virtual images below and real images above them. At wavelengths of the Mie resonances, these images have slightly better than ∼λ/4 resolution that can be attributed to the image magnification in close proximity to the object and contributions of its near field. The resonant super-resolution imaging of various point-like objects, such as dye molecules, fluorophores, or nanoplasmonic particles, can be realized by using narrow bandpass optical filters spectrally aligned with the Mie.
Generation of synthetic CT from CBCT using deep learning approaches for head and neck cancer patients

Purpose. To create a synthetic CT (sCT) from daily CBCT using either deep residual U-Net (DRUnet), or conditional generative adversarial network (cGAN) for adaptive radiotherapy planning (ART). Methods. First fraction CBCT and planning CT (pCT) were collected from 93 Head and Neck patients who underwent external beam radiotherapy. The dataset was divided into training, validation, and test sets of 58, 10 and 25 patients respectively. Three methods were used to generate sCT, 1. Nonlocal means patch based method was modified to include multiscale patches defining the multiscale patch based method (MPBM), 2. An encoder decoder 2D Unet with imbricated deep residual units was implemented, 3. DRUnet was integrated to the generator part of cGAN whereas a convolutional PatchGAN classifier was used as the discriminator. The accuracy of sCT was evaluated geometrically using Mean Absolute Error (MAE). Clinical Volumetric Modulated Arc Therapy (VMAT) plans were copied from pCT to registered CBCT and sCT and dosimetric analysis was performed by comparing Dose Volume Histogram (DVH) parameters of planning target volumes (PTVs) and organs at risk (OARs). Furthermore, 3D Gamma analysis (2%/2mm, global) between the dose on the sCT or CBCT and that on the pCT was performed. Results. The average MAE calculated between pCT and CBCT was 180.82 ± 27.37HU. Overall, all approaches significantly reduced the uncertainties in CBCT. Deep learning approaches outperformed patch-based methods with MAE = 67.88 ± 8.39HU (DRUnet) and MAE = 72.52 ± 8.43HU (cGAN) compared to MAE = 90.69 ± 14.3HU (MPBM). The percentages of DVH metric deviations were below 0.55% for PTVs and 1.17% for OARs using DRUnet. The average Gamma pass rate was 99.45 ± 1.86% for sCT generated using DRUnet. Conclusion. DL approaches outperformed MPBM. Specifically, DRUnet could be used for the generation of sCT with accurate intensities and realistic description of patient anatomy. This could be beneficial for CBCT based ART.
Health benefits of reducing ambient levels of fine particulate matter: a mortality impact assessment in Taiwan

ABSTRACT While numerous studies have found a relationship between long-term exposure to airborne fine particulate matter (PM2.5) and higher risk of death, few investigations examined the contribution that a reduction of exposure to ambient PM2.5 levels might exert on mortality rates. This study aimed to collect data on changes in annual average ambient levels of PM2.5 from 2006 to 2020 and consequent health impact in public health in 65 municipalities in Taiwan. Avoidable premature mortality was used here as an indicator of adverse health impact or health benefits. Annual PM2.5 levels were averaged for the years 2006, 2010, and 2020. In accordance with World Health Organization (WHO) methodology, differences were estimated in the number of deaths attributed to ambient PM2.5 exposure which were derived from concentration-response data from prior epidemiological studies. PM2.5 concentrations were found to have been decreased markedly throughout Taiwan over the two-decade study. As the PM2.5 concentrations fell, so was the health burden as evidenced by number of deaths concomitantly reduced from 22.4% in 2006 to 8.47% in 2020. Data demonstrated that reducing annual mean levels of PM2.5 to PM10 ug/m3 was associated with decrease in the total burden of mortality, with a 2.22–13.18% fall in estimated number of PM2.5-related deaths between 2006 and 2020. Based upon these results, these declines in ambient PM2.5 levels were correlated with significant improvement in public health (health benefits) and diminished number of deaths in Taiwan.
Kinetically controlled indium surface coverage effects on PAMBE-growth of InN/GaN(0001) quantum well structures

We report the effects of nitrogen (N) plasma and indium (In) flux on the In adatom adsorption/desorption kinetics on a GaN(0001) surface at the relatively high plasma-assisted molecular beam epitaxy-growth temperature of 680 °C. We experimentally demonstrate that under an active N flux, the (3×3)R30° surface reconstruction containing In and N quickly appears and the dynamically stable In adlayers sitting on this surface exhibit a continuous change from 0 to 2 MLs as a function of In flux. Compared to the bare GaN 1×1 surface which is stable during In exposure without an active N flux, we observed a much faster desorption for the bottom In adlayer and the absence of an In flux window corresponding to an In coverage of 1 ML. Moreover, when the In coverage exceeds 2 MLs, the desorption rates become identical for both surfaces. Finally, the importance of In surface coverage before GaN capping was shown by growing a series of InN/GaN multiple quantum well samples. The photoluminescence data show that a consistent quantum well structure is only formed if the surface is covered by excess In droplets before GaN capping.We report the effects of nitrogen (N) plasma and indium (In) flux on the In adatom adsorption/desorption kinetics on a GaN(0001) surface at the relatively high plasma-assisted molecular beam epitaxy-growth temperature of 680 °C. We experimentally demonstrate that under an active N flux, the (3×3)R30° surface reconstruction containing In and N quickly appears and the dynamically stable In adlayers sitting on this surface exhibit a continuous change from 0 to 2 MLs as a function of In flux. Compared to the bare GaN 1×1 surface which is stable during In exposure without an active N flux, we observed a much faster desorption for the bottom In adlayer and the absence of an In flux window corresponding to an In coverage of 1 ML. Moreover, when the In coverage exceeds 2 MLs, the desorption rates become identical for both surfaces. Finally, the importance of In surface coverage before GaN capping was shown by growing a series of InN/GaN multiple quantum well samples. The photoluminescence data show that a consi.
The bioengineering of perfusable endocrine tissue with anastomosable blood vessels

Organ transplantation is a definitive treatment for endocrine disorders, but donor shortages limit the use of this technique. The development of regenerative therapies would revolutionize the treatment of endocrine disorders. As is the case for harvested organs, the ideal bioengineered graft would comprise vascularized endocrine tissue, contain blood vessels that could be anastomosed to host vessels, have stable blood flow, and be suitable for transplantation into various sites. Here, we describe a transplantable endocrine tissue graft that was fabricated by ex vivo perfusion of tricultured cell sheets (islet β-cells, vascular endothelial cells (vECs), and mesenchymal stem cells (MSCs)) on a vascularized tissue flap of in vivo origin. The present study has three key findings. First, mild hypothermic conditions enhanced the success of ex vivo perfusion culture. Specifically, graft construction failed at 37 °C but succeeded at 32 °C (mild hypothermia), and endocrine tissue fabricated under mild hypothermia contained aggregations of islet β-cells surrounded by dense vascular networks. Second, the construction of transplantable endocrine tissue by ex vivo perfusion culture was better achieved using a vascular flap (VF) than a muscle flap. Third, the endocrine tissue construct generated using a VF could be transplanted into the rat by anastomosis of the graft artery and vein to host blood vessels, and the graft secreted insulin into the host’s circulatory system for at least two weeks after transplantation. Endocrine tissues bioengineered using these techniques potentially could be used as novel endocrine therapies.
Parents' perceptions: Participation patterns and desires for change for children and adolescents with autism spectrum disorder-A descriptive population-based study from Switzerland.

BACKGROUND Low participation in youth with autism spectrum disorder (ASD) has been reported, but age-related and contextual information is rare. OBJECTIVE This study aimed to describe, from parental perspectives, two patterns of participation and parental desires for change of children (age: 5-11) and adolescents (age: 12-17) with ASD in Switzerland. METHOD A cross-sectional design used the German version of the Participation and Environment Measure-Child and Youth to describe and juxtapose the participation results of 60 children and 55 adolescents with ASD in 45 activities at home, school and in the community and parental desires for change. RESULTS Participation patterns differed between settings and age groups. Both groups were found to participate most at home, followed by school, whereas community participation was either low or nonexistent. Children were more involved at home than adolescents, while school involvement was higher than participation frequency in both age groups. Community participation frequency was generally low but higher in children than in adolescents, while involvement was similarly low in both groups. Half the parents expressed desire for change with three tendencies: (1) widespread desire for change at home due to high support needs, (2) parents of adolescents expressed more desire for change in all settings than those of children and (3) all parents mainly desired to increase participation frequency and involvement. CONCLUSIONS This study informs research and social, health and community service providers to further reshape their programmes to meet parental needs and increase the participation of youth with ASD.
Multimodal Temporal Pattern Discrimination Is Encoded in Visual Cortical Dynamics

Abstract Discriminating between temporal features in sensory stimuli is critical to complex behavior and decision-making. However, how sensory cortical circuit mechanisms contribute to discrimination between subsecond temporal components in sensory events is unclear. To elucidate the mechanistic underpinnings of timing in primary visual cortex (V1), we recorded from V1 using two-photon calcium imaging in awake-behaving mice performing a go/no-go discrimination timing task, which was composed of patterns of subsecond audiovisual stimuli. In both conditions, activity during the early stimulus period was temporally coordinated with the preferred stimulus. However, while network activity increased in the preferred condition, network activity was increasingly suppressed in the nonpreferred condition over the stimulus period. Multiple levels of analyses suggest that discrimination between subsecond intervals that are contained in rhythmic patterns can be accomplished by local neural dynamics in V1.
Effects of Physical Activity in the High School Curriculum on Cardiovascular Health, Cognitive and Physical Performance

Cardiovascular health at a young age has implications for preventing cardiovascular disease, and it is associated with improved physical and cognitive performance during the aging process. Sports are well known to prevent cardiovascular disease; however, school-based interventions have mostly been neglected. This cross-sectional study aimed to compare groups of high school students, stratified by the amount of physical activity in their high school curriculum and downtime. Comparisons concerning physical and cognitive performance and arterial stiffness were made. A total of 63 senior-year students were investigated. Arterial stiffness was assessed using the oscillometric technique with ArteriographTM detection. Three-kilometer and pendulum runs were conducted as typical training loads. Cognitive performance was evaluated via the visual and verbal memory and number connection tests. Regarding cognitive skills, extracurricular physical activity improved the number connection test in male participants (p = 0.004). For physical performance, female students with a sports-focused curriculum were faster in the 3 km run (p < 0.001). Concerning arterial stiffness, the measurements yielded a lower mean arterial pressure (p = 0.015) and aortic pulse wave velocity (p = 0.04) in male students with a sports-focused curriculum. In summary, extracurricular physical activity and enrollment in a sports-focused curriculum may be associated with lower cardiovascular risk due to lower arterial stiffness and better physical and cognitive abilities.
Evaluation of 2 Artificial Intelligence Software for Chest X-Ray Screening and Pulmonary Tuberculosis Diagnosis: Protocol for a Retrospective Case-Control Study

Background According to the World Bank, Malaysia reported an estimated 97 tuberculosis cases per 100,000 people in 2021. Chest x-ray (CXR) remains the best conventional method for the early detection of pulmonary tuberculosis (PTB) infection. The intervention of artificial intelligence (AI) in PTB diagnosis could efficiently aid human interpreters and reduce health professionals’ work burden. To date, no AI studies have been evaluated in Malaysia. Objective This study aims to evaluate the performance of Putralytica and Qure.ai software for CXR screening and PTB diagnosis among the Malaysian population. Methods We will conduct a retrospective case-control study at the Respiratory Medicine Institute, National Cancer Institute, and Sungai Buloh Health Clinic. A total of 1500 CXR images of patients who completed treatments or check-ups will be selected and categorized into three groups: (1) abnormal PTB cases, (2) abnormal non-PTB cases, and (3) normal cases. These CXR images, along with their clinical findings, will be the reference standard in this study. All patient data, including sociodemographic characteristics and clinical history, will be collected prior to screening via Putralytica and Qure.ai software and readers’ interpretation, which are the index tests for this study. Interpretation from all 3 index tests will be compared with the reference standard, and significant statistical analysis will be computed. Results Data collection is expected to commence in August 2023. It is anticipated that 1 year will be needed to conduct the study. Conclusions This study will measure the accuracy of Putralytica and Qure.ai software and whether their findings will concur with readers’ interpretation and the reference standard, thus providing evidence toward the effectiveness of implementing AI in the medical setting. International Registered Report Identifier (IRRID) PRR1-10.2196/36121
Numerical studies of laser beam propagation with phase screen method using Non-Kolmogorov atmospheric turbulence

It is well known that atmospheric turbulence severely limits the applications based on the laser propagation though the atmosphere. The most common disturbances occurring due to the atmospheric turbulence are beam spreading, beam wandering, and scintillation. These effects are continuously changing in response to atmospheric conditions. In this study, we create a Non-Kolmogorov turbulence model which is based on the geometrical optics approximation and the property of Gamma function and integrate with in Gaussian beam analytically. This approach helps us to understand the propagation of the laser beam at different wavelengths in the atmospheric turbulence.