Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models

Transfer learning aims to leverage knowledge from pre-trained models to benefit the target task. Prior transfer learning work mainly transfers from a single model. However, with the emergence of deep models pre-trained from different resources, model hubs consisting of diverse models with various architectures, pre-trained datasets and learning paradigms are available. Directly applying single-model transfer learning methods to each model wastes the abundant knowledge of the model hub and suffers from high computational cost. In this paper, we propose a Hub-Pathway framework to enable knowledge transfer from a model hub. The framework generates data-dependent pathway weights, based on which we assign the pathway routes at the input level to decide which pre-trained models are activated and passed through, and then set the pathway aggregation at the output level to aggregate the knowledge from different models to make predictions. The proposed framework can be trained end-to-end with the target task-specific loss, where it learns to explore better pathway configurations and exploit the knowledge in pre-trained models for each target datum. We utilize a noisy pathway generator and design an exploration loss to further explore different pathways throughout the model hub. To fully exploit the knowledge in pre-trained models, each model is further trained by specific data that activate it, which ensures its performance and enhances knowledge transfer. Experiment results on computer vision and reinforcement learning tasks demonstrate that the proposed Hub-Pathway framework achieves the state-of-the-art performance for model hub transfer learning.Comment: Accepted by NeurIPS 202

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Integration of Green Energy and Advanced Energy-Efficient Technologies for Municipal Wastewater Treatment Plants

Wastewater treatment can consume a large amount of energy to meet discharge standards. However, wastewater also contains resources which could be recovered for secondary uses under proper treatment. Hence, the goal of this paper is to review the available green energy and biomass energy that can be utilized in wastewater treatment plants. Comprehensive elucidation of energy-efficient technologies for wastewater treatment plants are revealed. For these energy-efficient technologies, this review provides an introduction and current application status of these technologies as well as key performance indicators for the integration of green energy and energy-efficient technologies. There are several assessment perspectives summarized in the evaluation of the integration of green energy and energy-efficient technologies in wastewater treatment plants. To overcome the challenges in wastewater treatment plants, the Internet of Things (IoT) and green chemistry technologies for the water and energy nexus are proposed. The findings of this review are highly beneficial for the development of green energy and energy-efficient wastewater treatment plants. Future research should investigate the integration of green infrastructure and ecologically advanced treatment technologies to explore the potential benefits and advantages

Exploring the potential of ChatGPT as an adjunct for generating diagnosis based on chief complaint and cone beam CT radiologic findings

Abstract Aim This study aimed to assess the performance of OpenAI’s ChatGPT in generating diagnosis based on chief complaint and cone beam computed tomography (CBCT) radiologic findings. Materials and methods 102 CBCT reports (48 with dental diseases (DD) and 54 with neoplastic/cystic diseases (N/CD)) were collected. ChatGPT was provided with chief complaint and CBCT radiologic findings. Diagnostic outputs from ChatGPT were scored based on five-point Likert scale. For diagnosis accuracy, the scoring was based on the accuracy of chief complaint related diagnosis and chief complaint unrelated diagnoses (1–5 points); for diagnosis completeness, the scoring was based on how many accurate diagnoses included in ChatGPT’s output for one case (1–5 points); for text quality, the scoring was based on how many text errors included in ChatGPT’s output for one case (1–5 points). For 54 N/CD cases, the consistence of the diagnosis generated by ChatGPT with pathological diagnosis was also calculated. The constitution of text errors in ChatGPT’s outputs was evaluated. Results After subjective ratings by expert reviewers on a five-point Likert scale, the final score of diagnosis accuracy, diagnosis completeness and text quality of ChatGPT was 3.7, 4.5 and 4.6 for the 102 cases. For diagnostic accuracy, it performed significantly better on N/CD (3.8/5) compared to DD (3.6/5). For 54 N/CD cases, 21(38.9%) cases have first diagnosis completely consistent with pathological diagnosis. No text errors were observed in 88.7% of all the 390 text items. Conclusion ChatGPT showed potential in generating radiographic diagnosis based on chief complaint and radiologic findings. However, the performance of ChatGPT varied with task complexity, necessitating professional oversight due to a certain error rate

Root-Securing and Brain-Fortifying Liquid Upregulates Caveolin-1 in Cell Model with Alzheimer’s Disease through Inhibiting Tau Phosphorylation

In order to explore the effect of root-securing and brain-fortifying Liquid- (RSBFL-) mediated caveolin-1 (CAV-1) on phosphorylation of Tau protein and to uncover underlying mechanisms of RSBFL for the prevention and treatment of Alzheimer’s disease (AD), hippocampal neurons isolated from neonatal SD rats and cultured in DMEM-F12 medium were induced by exogenous Aβ1–42 to establish a cell model with AD. Meanwhile, pEGFP-C1-CAV1 and CAV1-shRNA plasmids were transfected into hippocampal neurons for CAV-1 overexpression and silence, respectively. The serum containing RSBFL was prepared for the intervention of AD model cells. The expression of CAV-1, GSK-3β, and p-Tau in normal hippocampal neurons and AD model cells in the presence of serum containing RSBFL was evaluated. The model hippocampal neurons with AD induced by Aβ1–42 revealed an obvious CAV-1 inhibition, enhanced GSK-3β activity, and abnormal Tau phosphorylation. In contrast, the treatment with serum containing RSBFL could upregulate CAV-1 in AD hippocampal neurons (P<0.05) with improved p-GSK-3βSer9 and reduced p-GSK-3βTyr216 (P<0.01), as well as suppressed abnormal phosphorylation of Tau protein. Therefore, RSBFL has an excellent protective effect on hippocampal neurons through increasing CAV-1 expression, inhibiting GSK-3β activity, and reducing excessive abnormal phosphorylation of Tau protein

Visualization 1: Tight focusing of femtosecond radially polarized light pulses through a dielectric interface

The propagation evolution of the radially polarized, ultrashort pulsed laser beams. Originally published in JOSA A on 01 September 2015 (josaa-32-9-1717

Two-Dimensional Cr5Te8@Graphite Heterostructure for Efficient Electromagnetic Microwave Absorption

Highlights A Cr5Te8@expanded graphite heterostructure is fabricated by chemical vapor deposition, exhibiting remarkable microwave absorption performance with a minimum reflection loss of up to − 57.6 dB at a thin thickness of only 1.4 mm under a low filling rate of 10%. Density functional theory calculations deeply reveal the polarization loss mechanism triggered by heterogeneous interfaces. The heterostructure coating displays a remarkable radar cross section reduction of 31.9 dB m2, demonstrating a great electromagnetic microwave scattering ability and radar stealth capability

Protective Effect of Trillium Tschonoskii Maxim Saponin on CCl4-induced Acute Liver Injury of Rats through Apoptosis Inhibition

In order to explore hepatoprotective role and underlying mechanisms of TTM, 36 rats were randomly divided into control, CCl4-induced liver injury model, and DDB and low, moderate and high-dose TTM treatment groups. After CCl4-induced model establishment, the rats from DDB and TTM groups were administrated with DDB at 0.2 g/kg⋅d and TTM at 0.1, 0.5 and 1.0 g/kg⋅d, while the rats from control and model groups were administrated with saline. Upon 5 days treatments, all rats were sacrificed for determining serum ALT and AST levels and liver index, examining histopathological changes in liver through HE and TUNEL staining, and evaluating TNF-α and IL-6 mRNA expression by RT-PCR, and Caspase-3, Bcl-2 and Bax expression by Western blot. Results indicated that CCl4 could induce acute liver injury and abnormal liver function in rats with obvious hepatomegaly, increased liver index, high ALT and AST levels, up-regulated TNF-α and IL-6, and overexpressed Bax and Caspase-3. However, DDB and TTM could execute protective role in CCl4-induced liver injury in rats through reducing ALT and AST levels, rescuing hepatomegaly, down-regulating inflammatory factors and inhibiting hepatocyte apoptosis in a dose-dependent manner. Therefore, TTM has obvious protective role in CCl4-induced liver injury of rats through inhibiting hepatocyte apoptosis.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author