A Conceptual Model of Trust Influencing Factors in Robo-Advisor Products: A Qualitative Study

As an integration of e-commerce and traditional financial service, robo-advisor is a promising product that recommends portfolios to individual investors based on modern technologies. However, this industry faces many challenges such as slow adoption and distrust from customers. This paper extends prior literatures in robo-advisor by exploring trust influencing factors and their detailed sub-factors from the perspective of five dimensions of trust. In this study, we not only validated previous factors of trust in the context of robo-advisor, but also found several new factors influencing customers’ feelings. A conceptual model is further proposed. The data analysis is based on semi-structured interviews with 27 investors. Understanding trust factors of robo-advisor helps the service vendors provide a better product for individual investors and facilitates faster adoption behavior from customers, which promotes further development of the industry

Quantifying Inactive Lithium in Lithium Metal Batteries

Inactive lithium (Li) formation is the immediate cause of capacity loss and catastrophic failure of Li metal batteries. However, the chemical component and the atomic level structure of inactive Li have rarely been studied due to the lack of effective diagnosis tools to accurately differentiate and quantify Li+ in solid electrolyte interphase (SEI) components and the electrically isolated unreacted metallic Li0, which together comprise the inactive Li. Here, by introducing a new analytical method, Titration Gas Chromatography (TGC), we can accurately quantify the contribution from metallic Li0 to the total amount of inactive Li. We uncover that the Li0, rather than the electrochemically formed SEI, dominates the inactive Li and capacity loss. Using cryogenic electron microscopies to further study the microstructure and nanostructure of inactive Li, we find that the Li0 is surrounded by insulating SEI, losing the electronic conductive pathway to the bulk electrode. Coupling the measurements of the Li0 global content to observations of its local atomic structure, we reveal the formation mechanism of inactive Li in different types of electrolytes, and identify the true underlying cause of low Coulombic efficiency in Li metal deposition and stripping. We ultimately propose strategies to enable the highly efficient Li deposition and stripping to enable Li metal anode for next generation high energy batteries

FPGA Deployment of LFADS for Real-time Neuroscience Experiments

Large-scale recordings of neural activity are providing new opportunities to study neural population dynamics. A powerful method for analyzing such high-dimensional measurements is to deploy an algorithm to learn the low-dimensional latent dynamics. LFADS (Latent Factor Analysis via Dynamical Systems) is a deep learning method for inferring latent dynamics from high-dimensional neural spiking data recorded simultaneously in single trials. This method has shown a remarkable performance in modeling complex brain signals with an average inference latency in milliseconds. As our capacity of simultaneously recording many neurons is increasing exponentially, it is becoming crucial to build capacity for deploying low-latency inference of the computing algorithms. To improve the real-time processing ability of LFADS, we introduce an efficient implementation of the LFADS models onto Field Programmable Gate Arrays (FPGA). Our implementation shows an inference latency of 41.97 $\mu$s for processing the data in a single trial on a Xilinx U55C.Comment: 6 pages, 8 figure

Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set

<p> Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was 98.1% +/- 1.1%.</p

Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial for the success in various downstream clinical tasks. However, current foundation models have only been evaluated on a limited type and number of tasks, leaving their generalization ability and overall performance unclear. To address this gap, we established a most comprehensive benchmark to evaluate the performance of off-the-shelf foundation models across six distinct clinical task types, encompassing a total of 39 specific tasks. Our findings reveal that existing foundation models excel at certain task types but struggle to effectively handle the full breadth of clinical tasks. To improve the generalization of pathology foundation models, we propose a unified knowledge distillation framework consisting of both expert and self knowledge distillation, where the former allows the model to learn from the knowledge of multiple expert models, while the latter leverages self-distillation to enable image representation learning via local-global alignment. Based on this framework, a Generalizable Pathology Foundation Model (GPFM) is pretrained on a large-scale dataset consisting of 190 million images from around 86,000 public H&E whole slides across 34 major tissue types. Evaluated on the established benchmark, GPFM achieves an impressive average rank of 1.36, with 29 tasks ranked 1st, while the the second-best model, UNI, attains an average rank of 2.96, with only 4 tasks ranked 1st. The superior generalization of GPFM demonstrates its exceptional modeling capabilities across a wide range of clinical tasks, positioning it as a new cornerstone for feature representation in CPath

Physiological ischemic training improves cardiac function through the attenuation of cardiomyocyte apoptosis and the activation of the vagus nerve in chronic heart failure

PurposeThis study investigated the functional outcomes of patients with chronic heart failure (CHF) after physiological ischemic training (PIT), identified the optimal PIT protocol, evaluated its cardioprotective effects and explored the underlying neural mechanisms.MethodsPatients with CHF were randomly divided into experimental group (n = 25, PIT intervention + regular treatment) and control group (n = 25, regular treatment). The outcomes included the left ventricular ejection fraction (LVEF), brain natriuretic peptide (BNP) and cardiopulmonary parameters. LVEF and cardiac biomarkers in CHF rats after various PIT treatments (different in intensity, frequency, and course of treatment) were measured to identify the optimal PIT protocol. The effect of PIT on cardiomyocyte programmed cell death was investigated by western blot, flow cytometry and fluorescent staining. The neural mechanism involved in PIT-induced cardioprotective effect was assessed by stimulation of the vagus nerve and muscarinic M2 receptor in CHF rats.ResultsLVEF and VO2max increased while BNP decreased in patients subjected to PIT. The optimal PIT protocol in CHF rats was composed of five cycles of 5 min ischemia followed by 5 min reperfusion on remote limbs for 8 weeks. LVEF and cardiac biomarker levels were significantly improved, and cardiomyocyte apoptosis was inhibited. However, these cardioprotective effects disappeared after subjecting CHF rats to vagotomy or muscarinic M2 receptor inhibition.ConclusionPIT improved functional outcomes in CHF patients. The optimal PIT protocol required appropriate intensity, reasonable frequency, and adequate treatment course. Under these conditions, improvement of cardiac function in CHF was confirmed through cardiomyocyte apoptosis reduction and vagus nerve activation

Protocatechuic acid: A novel detoxication agent of fumonisin B1 for poultry industry

Fumonisin B1 (FB1) is a major fusarium mycotoxin that largely contaminates feedstuffs and foods, posing a health risk to both animals and humans. This mycotoxin can enter the human body directly through contaminated food consumption or indirectly by toxins and their metabolites. In a prior study, feed-borne FB1 is one of the leading mycotoxins in breeder eggs, leading to reduced hatchability and gizzard ulceration in chicken progenies. Currently, no effective way is available to remove FB1 from feeds and human-contaminated foods. We hypothesize that FB1 can be reduced to low risk by protocatechuic acid (PCA). To assess the ability of FB1 to be degraded in vivo, 1 ppm of FB1 was treated with PCA, or D-glucose, or silymarin, or anti-FB1 monoclonal antibody. Our study revealed that both D-glucose and PCA exhibited 53.4 and 71.43% degradation, respectively, at 80°C for 2 h, while 35.15% of FB1 detoxification was determined in the silymarin group at 60°C for 0.5 h. A dose-dependent manner was found after treatment with D-glucose or PCA at 80°C for 2 h. As for detoxification of anti-FB1 monoclonal antibody, the 1:3,000 dilution induced significant FB1 detoxification, accounting for 25.9% degradation at 25°C for 2 h. Furthermore, 50 SPF 11-day-old embryonated eggs were divided into 10 groups, with five eggs per group. Post treatment with PCA or D-glucose, or silymarin or anti-FB1 monoclonal antibody, the treated samples were inoculated into albumens and monitored daily until the hatching day. Consequently, 100% of the chickens survived in the D-glucose group and other control groups, except for the FB1 control group, while 80, 80, and 60% hatching rates were found in the PCA-treated group, the anti-FB1 monoclonal antibody-treated group, and the silymarin-treated group. Additionally, both the FB1 group and the silymarin-treated group yielded lower embryo growth than other groups did. Postmortem, lower gizzard ulceration index was determined in the PCA-treated group and the anti-FB1 monoclonal antibody-treated group compared to those of the silymarin-treated group and D-glucose-treated group. Based on the above evidence, PCA is a promising detoxification to reduce FB1 contamination in the poultry industry, contributing to the eradication of mycotoxin residuals in the food chain and maintaining food security for human beings