CDK5 Upregulated by ELF3 Transcription Promotes IL-1ß-induced Inflammation and Extracellular Matrix Degradation in Human Chondrocytes.

Osteoarthritis (OA) is a common chronic disease with age-associated increase in both incidence and prevalence. The cyclin-dependent kinase 5 (CDK5), which is a member of the CDK family, is involved in many chronic diseases. This study was performed to explore the functional role of CDK5 in OA and to discuss the detailed molecular mechanisms. The expressions of CDK5 and ELF3 before or after transfection were detected with reverse transcription-quantitative PCR (RT-qPCR) and western blot. 5-ethynyl-2'-deoxyuridine (Edu) and terminal deoxynucleoitidyl transferase-mediated nick-end labeling (TUNEL) assays were used to detect the proliferation and apoptosis of C28/I2 cells. The levels of inflammatory cytokines were estimated using enzyme-linked immunosorbent assay (ELISA) while the expressions of proteins implicated in extracellular matrix (ECM) degradation- and apoptosis were detected using western blot. Additionally, the activity of CDK5 promoters and its binding with ELF3 were detected using luciferase activity assay and chromatin immunoprecipitation (CHIP) assay. In the present study, it was discovered that the mRNA and protein expressions of CDK5 were significantly increased in IL-1ß-induced C28/I2 cells. After depleting CDK5 expression, the apoptosis, inflammation and ECM in C28/I2 cells with IL-1ß induction were suppressed. It was also found that ELF3 expression was increased in IL-1ß-induced C28/I2 cells and acted as a transcription factor binding to the CDK5 promoter to regulate its transcriptional expression. The further experiments evidenced that ELF3 overexpression partially reversed the inhibitory effects of CDK5 deficiency on IL-1ß-induced apoptosis, inflammation and ECM in C28/I2 cells. Collectively, CDK5 that upregulated by ELF3 transcription could promote the development of OA.

CLANet: A comprehensive framework for cross-batch cell line identification using brightfield images.

Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct bio-batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL's feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental bio-batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing bio-batch effects in cell line identification.

FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling.

Car-following is a control process in which a following vehicle adjusts its acceleration to keep a safe distance from the lead vehicle. Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. To address this gap and promote the development of microscopic traffic flow modeling, we establish the first public benchmark dataset for car-following behavior modeling. This benchmark consists of more than 80 K car-following events extracted from five public driving datasets under the same criteria. To give an overview of current progress in car-following modeling, we implemented and tested representative baseline models within the benchmark. The established benchmark provides researchers with consistent data formats and metrics for cross-comparing different car-following models, coming with open datasets and codes.

Automated quantification of 3D wound morphology by machine learning and optical coherence tomography in type 2 diabetes.

Background: Driven by increased prevalence of type 2 diabetes and ageing populations, wounds affect millions of people each year, but monitoring and treatment remain limited. Glucocorticoid (stress hormones) activation by the enzyme 11ß-hydroxysteroid dehydrogenase type 1 (11ß-HSD1) also impairs healing. We recently reported that 11ß-HSD1 inhibition with oral AZD4017 improves acute wound healing by manual 2D optical coherence tomography (OCT), although this method is subjective and labour-intensive. Objectives: Here, we aimed to develop an automated method of 3D OCT for rapid identification and quantification of multiple wound morphologies. Methods: We analysed 204 3D OCT scans of 3 mm punch biopsies representing 24 480 2D wound image frames. A u-net method was used for image segmentation into 4 key wound morphologies: early granulation tissue, late granulation tissue, neo-epidermis, and blood clot. U-net training was conducted with 0.2% of available frames, with a mini-batch accuracy of 86%. The trained model was applied to compare segment area (per frame) and volume (per scan) at days 2 and 7 post-wounding and in AZD4017 compared to placebo. Results: Automated OCT distinguished wound tissue morphologies, quantifying their volumetric transition during healing, and correlating with corresponding manual measurements. Further, AZD4017 improved epidermal re-epithelialisation (by manual OCT) with a corresponding trend towards increased neo-epidermis volume (by automated OCT). Conclusion: Machine learning and OCT can quantify wound healing for automated, non-invasive monitoring in real-time. This sensitive and reproducible new approach offers a step-change in wound healing research, paving the way for further development in chronic wounds.

Effects of geometric attributes of horizontal and sag vertical curve combinations on freeway crash frequency.

The geometric design of the combinations of horizontal and sag vertical curves (sag combinations or sag combined curves) is vital to road safety. However, there is little research that investigates the safety effects of their geometric attributes based on the analysis of real-world crash data. To this end, the crash, traffic, geometric design, and roadway configuration data are collected from 157 sag combinations on six freeways in Washington State, during 2011-2017. Poisson, negative binomial (NB), hierarchical Poisson, and hierarchical NB models are developed for analyzing the crash frequency of sag combinations. The models are estimated and compared in the context of Bayesian inference. The results indicate that significant over-dispersion and cross-group heterogeneity exist in the crash data and that the hierarchical NB model yields the best overall performance. The parameter estimates show that: five geometric attributes, including horizontal curvature, vertical curvature, departure grade, the ratio of horizontal curvature to vertical curvature, and the layout of front dislocation, have significant effects on the crash frequency of sag combinations. Freeway section length, annual average daily traffic, and speed limits are also important predictors of crash frequency. The analysis results and the proposed model are useful for evaluating the safety performance of freeway sag combinations and optimizing their geometric design based on substantive safety evaluation.

Not getting in too deep: A practical deep learning approach to routine crystallisation image classification.

Using a relatively small training set of ~16 thousand images from macromolecular crystallisation experiments, we compare classification results obtained with four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources. We show that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative. We use eight classes to effectively rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions.

A novel resilience analysis methodology for airport networks system from the perspective of different epidemic prevention and control policy responses.

As the COVID-19 pandemic fades, the aviation industry is entering a fast recovery period. To analyze airport networks' post-pandemic resilience during the recovery process, this paper proposes a Comprehensive Resilience Assessment (CRA) model approach using the airport networks of China, Europe, and the U.S.A as case studies. The impact of COVID-19 on the networks is analyzed after populating the models of these networks with real air traffic data. The results suggest that the pandemic has caused damage to all three networks, although the damages to the network structures of Europe and the U.S.A are more severe than the damage in China. The analysis suggests that China, as the airport network with less network performance change, has a more stable level of resilience. The analysis also shows that the different levels of stringency policy in prevention and control measures during the epidemic directly affected the recovery rate of the network. This paper provides new insights into the impact of the pandemic on airport network resilience.

Label-free prediction of cell painting from brightfield images.

Cell Painting is a high-content image-based assay applied in drug discovery to predict bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic perturbations. We investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from brightfield input. We train and validate two deep learning models with a dataset representing 17 batches, and we evaluate on batches treated with compounds from a phenotypic set. The mean Pearson correlation coefficient of the predicted images across all channels is 0.84. Without incorporating features into the model training, we achieved a mean correlation of 0.45 with ground truth features extracted using a segmentation-based feature extraction pipeline. Additionally, we identified 30 features which correlated greater than 0.8 to the ground truth. Toxicity analysis on the label-free Cell Painting resulted a sensitivity of 62.5% and specificity of 99.3% on images from unseen batches. We provide a breakdown of the feature profiles by channel and feature type to understand the potential and limitations of label-free morphological profiling. We demonstrate that label-free Cell Painting has the potential to be used for downstream analyses and could allow for repurposing imaging channels for other non-generic fluorescent stains of more targeted biological interest.

An automated cell line authentication method for AstraZeneca global cell bank using deep neural networks on brightfield images.

Cell line authentication is important in the biomedical field to ensure that researchers are not working with misidentified cells. Short tandem repeat is the gold standard method, but has its own limitations, including being expensive and time-consuming. Deep neural networks achieve great success in the analysis of cellular images in a cost-effective way. However, because of the lack of centralized available datasets, whether or not cell line authentication can be replaced or supported by cell image classification is still a question. Moreover, the relationship between the incubation times and cellular images has not been explored in previous studies. In this study, we automated the process of the cell line authentication by using deep learning analysis of brightfield cell line images. We proposed a novel multi-task framework to identify cell lines from cell images and predict the duration of how long cell lines have been incubated simultaneously. Using thirty cell lines' data from the AstraZeneca Cell Bank, we demonstrated that our proposed method can accurately identify cell lines from brightfield images with a 99.8% accuracy and predicts the incubation durations for cell images with the coefficient of determination score of 0.927. Considering that new cell lines are continually added to the AstraZeneca Cell Bank, we integrated the transfer learning technique with the proposed system to deal with data from new cell lines not included in the pre-trained model. Our method achieved excellent performance with a precision of 97.7% and recall of 95.8% in the detection of 14 new cell lines. These results demonstrated that our proposed framework can effectively identify cell lines using brightfield images.

Real-time crash identification using connected electric vehicle operation data.

As the automobile market gradually develops towards intelligence, networking, and information-orientated, intelligent identification based on connected vehicle data becomes a key technology. Specifically, real-time crash identification using vehicle operation data can enable automotive companies to obtain timely information on the safety of user vehicle usage so that timely customer service and roadside rescue can be provided. In this paper, an accurate vehicle crash identification algorithm is developed based on machine learning techniques using electric vehicles' operation data provided by SAIC-GM-Wuling. The point of battery disconnection is identified as a potential crash event. Data before and after the battery disconnection is retrieved for feature extraction. Two different feature extraction methods are used: one directly extracts the descriptive statistical features of various variables, and the other directly unfolds the multivariate time series data. The AdaBoost algorithm is used to classify whether a potential crash event is a real crash using the constructed features. Models trained with the two different features are fused for the final outputs. The results show that the final model is simple, effective, and has a fast inference speed. The model has an F1 score of 0.98 on testing data for crash classification, and the identified crash times are all within 10 s around the true crash times. All data and code are available at https://github.com/MeixinZhu/vehicle-crash-identification.

CANet: Context Aware Network for Brain Glioma Segmentation.

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets.

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration.

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

Biochars prepared from rabbit manure for the adsorption of rhodamine B and Congo red: characterisation, kinetics, isotherms and thermodynamic studies.

Biochar was prepared from rabbit faeces (RFB550) at 550 °C through pyrolysis and was characterised using elemental analysis, scanning electron microscopy, Brunauer-Emmett-Teller analysis and Fourier transform infrared spectroscopy (FTIR). The related factors, kinetics, isothermal curves and thermodynamics of the adsorption behaviours were investigated by conducting batch experiments. The results revealed the adsorption equilibrium of rhodamine B (RhB) and Congo red (CR) onto RFB550 with initial concentrations of 30 mg · L-1 at 25 °C and 210 min, and the best adsorption was observed when the pH of the RhB and CR solutions was 3 and 5, respectively. Pseudo-second-order kinetics was the most suitable model for describing the adsorption of RhB and CR onto RFB550, indicating that the rate-limiting step was mainly chemical adsorption. The isotherm data were best described by the Freundlich model, and the adsorption process was multi-molecular layer adsorption. Thermodynamic parameters revealed the spontaneous adsorption of RhB and CR onto RFB550. According to the results of the FTIR analysis, the oxygen-containing functional groups and aromatic structures on the surface of RFB550 provided abundant adsorption sites for RhB and CR, and the adsorption mechanism was potentially related to the hydrogen bonds and π-π bonds.

Finding the de-carbonization potentials in the transport sector: application of scenario analysis with a hybrid prediction model.

De-carbonization of the transport sector is an important pathway to climate-change mitigation and presents the potential for future lower emissions. To assess the potential quantitatively under different optimization measures, this paper presents a hybrid model combining an integrated machine learning model with the scenario analysis. We compare the training accuracy of the back-propagation neural networks (BPNN), Gaussian process regression (GPR), and support vector machine (SVM) fitting model with different training datasets. The results indicate that the performance of the SVM model is superior to other methods. And the particle swarm optimization (PSO) algorithm is then used to optimize hyper-parameters of the SVM model. Two scenarios including business as usual (BAU) and best case (BC) are set according to the current trends and target trends of driving factors identified by the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model. Finally, to find the de-carbonization potentials in the transport sector, the PSO-SVM model is applied to predict transport emissions from 2015 to 2030 under two scenarios. Results show that transport emissions reduce by about 131.36 million tons during 2015-2020 and 372.86 million tons during 2021-2025 in the BC scenario. The findings can effectively track, test, and predict the achievement of policy goals and provide practical guidance for de-carbonization development.

Travel demand and distance analysis for free-floating car sharing based on deep learning method.

In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network (LSTM-RNN), hourly variation in short-term traffic characteristics including travel demand and travel distance are modeled. The results were also compared with other different statistical models, such as support vector regression (SVR), Autoregressive Integrated Moving Average model (ARIMA), single and second exponential smoothing. It showed that (LSTM-RNN) shows better performance in terms of statistical analysis and tendency precision based on limited data sample.

IA-Lab: A MATLAB framework for efficient microscopy image analysis development, applied to quantifying intracellular transport of internalized peptide-drug conjugate.

This work presents a MATLAB-based software package for high-throughput microscopy image analysis development, making such development more accessible for a large user community. The toolbox provides a GUI and a number of analysis workflows, and can serve as a general framework designed to allow for easy extension. For a new application, only a minor part of the object-oriented code needs to be replaced by new components, making development efficient. This makes it possible to quickly develop solutions for analysis not available in existing tools. We show its use in making a tool for quantifying intracellular transport of internalized peptide-drug conjugates. The code is freely available as open source on GitHub (https://github.com/amcorrigan/ia-lab).

Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up Approach.

The increasing availability of big Automatic Identification Systems (AIS) sensor data offers great opportunities to track ship activities and mine spatial-temporal patterns of ship traffic worldwide. This research proposes a data integration approach to construct Global Shipping Networks (GSN) from massive historical ship AIS trajectories in a completely bottom-up way. First, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is applied to temporally identify relevant stop locations, such as marine terminals and their associated events. Second, the semantic meanings of these locations are obtained by mapping them to real ports as identified by the World Port Index (WPI). Stop events are leveraged to develop travel sequences of any ship between stop locations at multiple scales. Last, a GSN is constructed by considering stop locations as nodes and journeys between nodes as links. This approach generates different levels of shipping networks from the terminal, port, and country levels. It is illustrated by a case study that extracts country, port, and terminal level Global Container Shipping Networks (GCSN) from AIS trajectories of more than 4000 container ships in 2015. The main features of these GCSNs and the limitations of this work are finally discussed.

Ethanol-Quenching Introduced Oxygen Vacancies in Strontium Titanate Surface and the Enhanced Photocatalytic Activity.

Modification of the surface properties of SrTiO3 crystals by regulating the reaction environment in order to improve the photocatalytic activity has been widely studied. However, the development of a facile, effective, and universal method to improve the photocatalytic activity of these crystals remains an enormous challenge. We have developed a simple method to modify the surface environment of SrTiO3 by ethanol quenching, which results in enhanced UV, visible and infrared light absorption and photocatalytic performance. The SrTiO3 nanocrystals were preheated to 800 °C and immediately quenched by submersion in ethanol. X-ray diffraction patterns, electron paramagnetic resonance spectra, and X-ray photoelectron spectra indicated that upon rapid ethanol quenching, the interaction between hot SrTiO3 and ethanol led to the introduction of a high concentration of oxygen vacancies on the surface of the SrTiO3 lattice. Consequently, to maintain the regional charge balance of SrTiO3, Sr2+ could be substituted for Ti4+. Moreover, oxygen vacancies induced localized states into the band gap of the modified SrTiO3 and acted as photoinduced charge traps, thus promoting the photocatalytic activity. The improved photocatalytic performance of the modified SrTiO3 was demonstrated by using it for the decomposition of rhodamine B and production of H2 from water under visible or solar light.

A hybrid neural network for large-scale expressway network OD prediction based on toll data.

Accurate Origin-Destination (OD) prediction is significant for effective traffic monitor, which can support operation decision in traffic planning and management field. The enclosed expressway network system like toll gates system in China can collect mounts of trip records which can be gathered for OD prediction. The paper develops a novel neural network, which is named Expressway OD Prediction Neural Network (EODPNN) for toll data-based prediction. The network consists of the following three modules: The Feature Extension Module, the Memory Module, and the Prediction Module. In the process, the attributes data which can reflect the city attribute such as GDP, population, and the number of vehicles are considered to embeded into the notwork to increase the accuracy of the model. For the applicability improvment of the model, we categorize the cities in multiple classes based on their economy and population scales in this paper, which can provide a higher accurate prediction of OD by EODPNN. The results shows that, comparing to the traditional model like ARIMA and SVM, or typical neural networks like Bidirectional Long Short-term Memory, the EODPNN delivers a better prediction performance. The method proposed in this paper has been fully verified and has a potential to transplant to the other OD data-based management systems for a more accurate and flexible prediction.