An 8-week experiment was performed to investigate the influence on growth performance, plasma biochemistry, glucose metabolism and the insulin pathway of supplementation of dietary taurine to a high-carbohydrate diet for grass carp. In this study, fish were fed diets at one of two carbohydrate levels, 31·49 % (positive control) or 38·61 % (T00). The high-carbohydrate basal diet (T00), without taurine, was supplemented with 0·05 % (T05), 0·10 % (T10), 0·15 % (T15) or 0·20 % (T20) taurine, resulting in six isonitrogenous (30·37 %) and isolipidic (2·37 %) experimental diets. The experimental results showed that optimal taurine level improved significantly weight gain, specific growth rate (SGR), feed utilisation, reduced plasma total cholesterol levels, TAG and promoted insulin-like growth factor level. Glucokinase, pyruvate kinase and phosphoenolpyruvate carboxykinase activities showed a quadratic function model with increasing dietary taurine level, while hexokinase, fatty acid synthetase activities exhibited a positive linear trend. Optimal taurine supplementation in high-carbohydrate diet upregulated insulin receptor (Ir), insulin receptor substrate (Irs1), phosphatidylinositol 3-kinase (pi3k), protein kinase B (akt1), glycogen synthase kinase 3 β (gs3kβ) mRNA level and downregulated insulin-like growth factor (igf-1), insulin-like growth factor 1 receptor (igf-1R) and Fork head transcription factor 1 (foxo1) mRNA level. The above results suggested that optimal taurine level could improve growth performance, hepatic capacity for glycolipid metabolism and insulin sensitivity, thus enhancing the utilisation of carbohydrates in grass carp. Based on SGR, dietary optimal tributyrin taurine supplementation in grass carp was estimated to be 0·08 %.

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9–80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0–34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.

In droplet impacts, transitions between coalescence and bouncing are determined by complex interplays of multiple mechanisms dominating at various length scales. Here we investigate the mechanisms and governing parameters comprehensively by experiments and scaling analyses, providing a unified framework for understanding and predicting the outcomes when using different fluids. Specifically, while bouncing had not been observed in head-on collisions of water drops under atmospheric conditions, it was found in our experiments to appear on increasing the droplet diameter sufficiently. Contrarily, while bouncing was always observed in head-on impacts of alkane drops, we found it to disappear on decreasing the diameter sufficiently. The variations are related to gas draining dynamics in the inter-droplet film and suggest an easier means for controlling bouncing as compared to alternating the ambient pressure usually sought. The scaling analysis further shows that for a given Weber number, enlarging droplet diameter or fluid viscosities, or lowering surface tension contributes to a larger characteristic minimum thickness of the gas film, thus enhancing bouncing. The key dimensionless group $(O{h_{g,l}},\;O{h_l},\;{A^\ast })$ is identified, referred to as the two-phase Ohnesorge number, the Ohnesorge number of liquid and the Hamaker constant, respectively. Our thickness-based model indicates that as ${h^{\prime}_{m,c}} > 21.1{h_{cr}}$, where ${h^{\prime}_{m,c}}$ is the maximum value of the characteristic minimum film thickness $({h_{m,c}})$ and ${h_{cr}}$ is the critical thickness, bouncing occurs in both head-on and off-centre collisions. That is, when $1.2O{h_{g,l}}/(1 - 2O{h_l}) > \sqrt[3]{{{A^\ast }}}$, a fully developed bouncing regime occurs, thereby yielding a lower coalescence efficiency. The transitional Weber number is found universally to be 4.

ABSTRACT IMPACT: This poster will demonstrate how a community issue from a CTSI Community Advisory Board member organization initiated a collaborative, community-engaged project to identify priority areas of concern and culturally appropriate mitigation strategies. OBJECTIVES/GOALS: Little is known about the health and psychosocial impact of construction on older adults living near construction sites. We applied a mixed methods approach to identify evidence-based strategies to mitigate community prioritized health and psychosocial concerns related to long-term construction on older adults in NYC’s Manhattan Chinatown. METHODS/STUDY POPULATION: In Chinatown, where approximately 20% of its residents are seniors, many are poor, have a disability, and experience ambulatory difficulties. We used a mixed methods approach including: 1) a high level scoping review of the published literature on the health impact of long-term construction for older adults; 2) key informant interviews with stakeholders; and 3) a two-part community-engaged modified Delphi process to identify priority topic areas related to construction and older adults and evidence-informed, culturally-relevant mitigation strategies. Using priority areas identified through the modified Delphi process, we conducted a literature review on the health and psychosocial impact of construction on older adults. RESULTS/ANTICIPATED RESULTS: We identified five priority topics: construction site emissions; noise; outdoor nocturnal lighting; neighborhood changes; and relocation. Long-term construction is associated with environmental and psychosocial consequences with greater negative impacts on vulnerable populations. Current NYC mitigation policies are based on general population and need revisions to consider impacts for the most vulnerable, e.g. older adults and children, to mitigate adverse health outcomes. Findings were shared with City Council members and resulted in enacting specific recommended mitigation strategies, e.g. double paned windows, etc. Seniors are highly susceptible to the effects of air pollution, noise, and environmental changes, with exposure associated with higher morbidity, mortality, and social isolation. DISCUSSION/SIGNIFICANCE OF FINDINGS: Long-term construction may pose serious health implications for seniors residing near construction sites. Standards and guidelines for the general population may not protect them. Community-driven coalitions, like community-academic partnerships, can successfully advance community priorities and inform strategies to protect the elderly.

Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

Deep learning methods have been applied to randomly generate images, such as in fashion, furniture design. To date, consideration of human aspects which play a vital role in a design process has not been given significant attention in deep learning approaches. In this paper, results are reported from a human- in-the-loop design method where brain EEG signals are used to capture preferable design features. In the framework developed, an encoder extracting EEG features from raw signals recorded from subjects when viewing images from ImageNet are learned. Secondly, a GAN model is trained conditioned on the encoded EEG features to generate design images. Thirdly, the trained model is used to generate design images from a person's EEG measured brain activity in the cognitive process of thinking about a design. To verify the proposed method, a case study is presented following the proposed approach. The results indicate that the method can generate preferred designs styles guided by the preference related brain signals. In addition, this method could also help improve communication between designers and clients where clients might not be able to express design requests clearly.

Introduction: To investigate the effects of paroxetine (PAR) on motor and cognitive function recovery in patients with non-depressed ischemic stroke (nD-AIS).

Methods: One hundred sixty-seven patients hospitalized for non-depressed acute ischemic stroke were selected and divided into treatment (T) and control (C) groups using a random number table. All patients received conventional secondary ischemic stroke prevention and rehabilitation training; patients in Group T additionally received treatment with PAR (10 mg/day during week 1 and 20 mg/day thereafter) for 3 months. The follow-up observation lasted 6 months. The Fugl–Meyer motor scale (FMMS), Montreal cognitive assessment (MoCA), and Hamilton depression scale (HAMD) were used on D0, D15, D90, and D180 (T0, 1, 2, and 3, respectively; D180 = 90 days after treatment cessation) after study initiation, and scores were compared between the groups.

Results: The FMMS and MoCA scores differed significantly between Groups T and C at T2 and T3 (p < .05); by contrast, these scores did not differ significantly between the groups at T1 (p > .05). Furthermore, the HAMD scores differed significantly between the two groups at T3 (p < .05), but not at T1 and T2 (p > .05).

Conclusions: PAR treatment may improve motor and cognitive function recovery in patients with nD-AIS. Moreover, PAR may reduce the occurrence of depression after stroke.

Early life environments interact with genotype to determine stable phenotypic outcomes. Here we examined the influence of a variant in the brain-derived neurotropic factor (BDNF) gene (Val66Met), which underlies synaptic plasticity throughout the central nervous system, on the degree to which antenatal maternal anxiety associated with neonatal DNA methylation. We also examined the association between neonatal DNA methylation and brain substructure volume, as a function of BDNF genotype. Infant, but not maternal, BDNF genotype dramatically influences the association of antenatal anxiety on the epigenome at birth as well as that between the epigenome and neonatal brain structure. There was a greater impact of antenatal maternal anxiety on the DNA methylation of infants with the methionine (Met)/Met compared to both Met/valine (Val) and Val/Val genotypes. There were significantly more cytosine–phosphate–guanine sites where methylation levels covaried with right amygdala volume among Met/Met compared with both Met/Val and Val/Val carriers. In contrast, more cytosine–phosphate–guanine sites covaried with left hippocampus volume in Val/Val infants compared with infants of the Met/Val or Met/Met genotype. Thus, antenatal Maternal Anxiety × BDNF Val66Met Polymorphism interactions at the level of the epigenome are reflected differently in the structure of the amygdala and the hippocampus. These findings suggest that BDNF genotype regulates the sensitivity of the methylome to early environment and that differential susceptibility to specific environmental conditions may be both tissue and function specific.

Au nanoparticles (Au NPs) have attracted much interest owing to their unique optical properties. In this paper, a facile process has been successfully developed to synthesize the SiO2/Au hybrid microspheres with a diameter of 200 nm via the galvanic replacement of SiO2/Ag hybrid microspheres and chlorauric acid (HAuCl4) solution. The as-prepared products were investigated by x-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM, JEOL-6700F), and transmission electron microscopy (TEM, JEOL 3010), respectively. As expected, the as-prepared SiO2/Au hybrid microspheres show strong chemical stability and superior catalytic reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP). The SiO2/Au hybrid microspheres would be found widely used in wastewater treatment, catalytic reaction, bacteriostatic and bactericidal applications.

Using the discrete ordinate method, we solve the equations of phonon radiative transfer for thin films analytically, and deduce closed-form expressions for three thermophysical properties including the phonon intensity, heat flux density and effective phonon thermal conductivity. These analytical expressions provide a fast and convenient way to analyze heat conduction behavior in thin films, and thus allowing us to comprehend how the film thickness and bulk phonon mean free path affects the thermophysical properties. Also we extend these expressions from thin films to one period of superlattices, and demonstrate that adjusting the thickness ratio of two layers can control its effective phonon thermal conductivity.