OrganoID: a versatile deep learning platform for organoid image analysis

Organoids are three-dimensional in vitro tissue models that closely represent the native heterogeneity, microanatomy, and functionality of an organ or diseased tissue. Analysis of organoid morphology, growth, and drug response is challenging due to the diversity in shape and size of organoids, movement through focal planes, and limited options for live-cell staining. Here, we present OrganoID, an open-source image analysis platform that automatically recognizes, labels, and tracks single organoids in brightfield and phase-contrast microscopy. The platform identifies organoid morphology pixel by pixel without the need for fluorescence or transgenic labeling and accurately analyzes a wide range of organoid types in time-lapse microscopy experiments. OrganoID uses a modified u-net neural network with minimal feature depth to encourage model generalization and allow fast execution. The network was trained on images of human pancreatic cancer organoids and was validated on images from pancreatic, lung, colon, and adenoid cystic carcinoma organoids with a mean intersection-over-union of 0.76. OrganoID measurements of organoid count and individual area concurred with manual measurements at 96% and 95% agreement respectively. Tracking accuracy remained above 89% over the duration of a four-day validation experiment. Automated single-organoid morphology analysis of a dose-response experiment identified significantly different organoid circularity after exposure to different concentrations of gemcitabine. The OrganoID platform enables straightforward, detailed, and accurate analysis of organoid images to accelerate the use of organoids as physiologically relevant models in high-throughput research.

Mobile app users' privacy concerns: different heuristics for privacy assurance statements in the EU and China

PurposeAs mobile apps request permissions from users, protecting mobile users' personal information from being unnecessarily collected and misused becomes critical. Privacy regulations, such as General Data Protection Regulation in the European Union (EU), aim to protect users' online information privacy. However, one’s understanding of whether these regulations effectively make mobile users less concerned about their privacy is still limited. This work aims to study mobile users' privacy concerns towards mobile apps by examining the effects of general and specific privacy assurance statements in China and the EU.Design/methodology/approachDrawing on ecological rationality and heuristics theory, an online experiment and a follow-up validation experiment were conducted in the EU and China to examine the effects of privacy assurance statements on mobile users' privacy concerns.FindingsWhen privacy regulation is presented, the privacy concerns of Chinese mobile users are significantly lowered compared with EU mobile users. This indicates that individuals in the two regions react differently to privacy assurances. However, when a general regulation statement is used, no effect is observed. EU and Chinese respondents remain unaffected by general assurance statements.Originality/valueThis study incorporates notions from fast and frugal heuristics end ecological rationality – where seemingly irrational decisions may make sense in different societal contexts.

FB-CGANet: filter bank Channel Group Attention network for multi-class motor imagery classification

Objective. Motor imagery-based brain computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography (EEG) signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding. Approach. A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method Channel Group Attention (CGA) to build a lightweight neural network Filter Bank Channel Group Attention Network (FB-CGANet). Accompanied with FB-CGANet, the Band Exchange data augmentation method was proposed to generate training data for networks with filter bank structure. Main results. The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment. Significance. This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.

An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network

Background Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. Results In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. Conclusions The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.

M2PP: a novel computational model for predicting drug-targeted pathogenic proteins

Background Detecting pathogenic proteins is the origin way to understand the mechanism and resist the invasion of diseases, making pathogenic protein prediction develop into an urgent problem to be solved. Prediction for genome-wide proteins may be not necessarily conducive to rapidly cure diseases as developing new drugs specifically for the predicted pathogenic protein always need major expenditures on time and cost. In order to facilitate disease treatment, computational method to predict pathogenic proteins which are targeted by existing drugs should be exploited. Results In this study, we proposed a novel computational model to predict drug-targeted pathogenic proteins, named as M2PP. Three types of features were presented on our constructed heterogeneous network (including target proteins, diseases and drugs), which were based on the neighborhood similarity information, drug-inferred information and path information. Then, a random forest regression model was trained to score unconfirmed target-disease pairs. Five-fold cross-validation experiment was implemented to evaluate model’s prediction performance, where M2PP achieved advantageous results compared with other state-of-the-art methods. In addition, M2PP accurately predicted high ranked pathogenic proteins for common diseases with public biomedical literature as supporting evidence, indicating its excellent ability. Conclusions M2PP is an effective and accurate model to predict drug-targeted pathogenic proteins, which could provide convenience for the future biological researches.

Research on the End-Face Distribution of Rotational Molding Heating Gun Based on Numerical Simulation Method

The distribution of heating gun ends plays a decisive role in the sidewall properties of finished rotomolded products. To obtain the optimal distribution of the end face of a rotational molding heating gun, the temperature response of the end-face mold under heating gun heating was investigated, and an analysis method based on numerical simulation is proposed. The FDS (fire dynamics simulator) was used to construct a heating model of the heating gun, simulate and obtain a heatmap of the temperature field distribution of a heating gun of Φ30–70 mm, and determine the optimal diameter and heating distance of the heating gun. ANSYS was used to establish the thermal response model of the heat-affected mold, which was combined with the mold structure and thermophysical properties of steel. A temperature field distribution on the inner wall surface of Φ30, Φ50, and Φ70 mm heating guns when heating at each diameter of the end face was obtained and the distribution position of the end face of each diameter heating gun was determined. ANSYS was used to establish the thermal response model of the end-face mold and obtain the temperature field distribution of the inner wall surface of the end-face mold. The size of the heat-affected area of each diameter heating gun was combined, the end-face heating gun distribution was optimized, and the optimal heating gun end-face distribution was obtained. An experimental platform was built, and a validation experiment was set up. Through the analysis and processing of the data of three experiments, the temperature variation curve of each diameter on the inner surface of the end-face mold was obtained. We compare and analyze the simulation and experimental results to determine the feasibility of the FDS + ANSYS method and the correctness and accuracy of the simulation model and the results.

Natural Language Processing–Assisted Literature Retrieval and Analysis for Combination Therapy in Cancer

PURPOSE Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain.

The research of EEG feature extraction and classification for subjects with different organizational commitment

With the development of EEG analysis technology, researchers have gradually explored the correlation between personality trait (such as Big Five personality) and EEG. However, there are still many challenges in model construction. In this paper, we tried to classify the people with different organizational commitment personality trait through EEG. Firstly, we organized the participants to complete the organizational commitment questionnaire and recorded their resting state EEG. We divided 10 subjects into two classes (positive and negative) according to the questionnaire scores. Then, various EEG features including power spectral density, microstate, functional brain network and nonlinear features from segmented EEG sample were extracted as the input of different machine learning classifiers. Next, several evaluation metrics were used to evaluate the results of the cross-validation experiment. Finally, the results show that the EEG power in α band, the weighted clustering coefficient of functional brain network and the Permutation Entropy of EEG are relatively good features for this classification task. Furthermore, the highest classification accuracy rate can reach 79.9% with 0.87 AUC (the area under the ROC). The attempts in this paper may serve as the basis for our future research.

Experimental Investigations On Optimizing Manufacturing Parameters For Electro-Spark Deposition Diamond Wire Saw

The third generation of superhard semiconductor materials, represented by single-crystal SiC, is used widely in microelectronics due to their excellent physical and mechanical properties. However, their high hardness and brittleness become the bottleneck of their development. Diamond wire saw (DWS) has become the mainstream tool for sawing hard and brittle crystal materials. However, the diamond abrasive is consolidated on the core wire through resin or electroplated nickel, and the holding strength is not high. When sawing superhard crystal materials, the efficiency is low. In order to improve the sawing efficiency of superhard crystal materials, it is of great significance to improve the wear resistance of wire saw and the holding strength of abrasive particles. Electro-spark deposition (ESD) can deposit electrode materials on the substrate with low heat input to achieve metallurgical bonding between metal materials. It can effectively improve the gripping strength of the abrasive grains. And the sawing ability of the wire saw to make the consolidated DWS by the ESD process. In this paper, the ESD equipment has been designed according to the characteristics of the ESDDWS process. The discharge gap size and electrode consumption are monitored in real-time by a single-chip microcomputer (SCM). Orthogonal experiments were carried out for the two motion modes. The effects of process parameters, such as (A) Grain size, (B) Abrasive content, (C) Pulse duration time, (D) Compacting pressure, (E) Current, (F) Electrode diameter, (G) Pulse interval time, (H) Reciprocating times, (I) Wire feed speed, on the quality of ESDDWS were analyzed. Through the extremum difference analysis, the optimal parameter combinations of ESDDWS were obtained. The results of the validation experiment are better than the original experimental results.