Miracle-dz/UniFAR-FTUs huggingface dataset:
Putative host-derived insertions in the genomes of circulating SARS-CoV-2 variants

Insertions in the SARS-CoV-2 genome have the potential to drive viral evolution, but the source of the insertions is often unknown. Recent proposals have suggested that human RNAs could be a source of some insertions, but the small size of many insertions makes this difficult to confirm. Through an analysis of available direct RNA sequencing data from SARS-CoV-2 infected cells, we show that viral-host chimeric RNAs are formed through what are likely stochastic RNA-dependent RNA polymerase template switching events. Through an analysis of the publicly available GISAID SARS-CoV-2 genome collection, we identified two genomic insertions in circulating SARS-CoV-2 variants that are identical to regions of the human 18S and 28S rRNAs. These results provide direct evidence of the formation of viral-host chimeric sequences and the integration of host genetic material into the SARS-CoV-2 genome, highlighting the potential importance of host-derived insertions in viral evolution. IMPORTANCE Throughout the COVID-19 pandemic, the sequencing of SARS-CoV-2 genomes has revealed the presence of insertions in multiple globally circulating lineages of SARS-CoV-2, including the Omicron variant. The human genome has been suggested to be the source of some of the larger insertions, but evidence for this kind of event occurring is still lacking. Here, we leverage direct RNA sequencing data and SARS-CoV-2 genomes to show host-viral chimeric RNAs are generated in infected cells and two large genomic insertions have likely been formed through the incorporation of host rRNA fragments into the SARS-CoV-2 genome. These host-derived insertions may increase the genetic diversity of SARS-CoV-2 and expand its strategies to acquire genetic materials, potentially enhancing its adaptability, virulence, and spread.
Multi-context 3D Resnet for Small-size False Positive Reduction in Pelvic Lymph Node Detection

False positive reduction(FPR) plays a crucial role in abdominal lymph node detection system, which is of great significance in colorectal cancer diagnosis and early treatment. However, this remains a challenge owing to the complexity of the abdominal tissue. In this study, a simple yet effective method for FPR in the small-size abdominal lymph nodes detection is proposed. For small-size lymph nodes, we design a 3D residual network to adapt to corresponding input, and conveniently adjust the network structure and parameters according to the amount of data. Moreover, we use a multi-context fusion method to integrate the results of multiple models to meet the challenge of vary in lymph node volume. Due to the open-source CTLNDataset with only large lymph node, we utilize a new dataset named PLN-Dataset, which contains a large number of small-size pelvic lymph nodes. The proposed method gets an area under curve(AUC) value of 0.991 in PLNDataset, a good performance metric on the competition performance metric(CPM) with a score of 0.837, and also achieve competitive results in CTLNDataset. The result shows that the proposed method is effective and robust for small-size abdominal lymph nodes.
Novel Wheel Skid Detection Method without Chassis Velocity for Electric Vehicle

Novel wheel skid detection method for electric vehicle (EV) is proposed and experimentally evaluated. This proposed method can detect the skid of wheel with only the values of current and rotating velocity of each motor. Estimation of vehicle chassis velocity or wheel absolute velocity is not required any more, and this is a remarkable advantage over conventional methods for anti skid brake system (ABS) or traction control system (TCS). In this proposed method, skid is detected with the dependence of traction force on motor torque. Traction force is estimated with simple disturbance observer. Therefore, the exact value of shaft input torque is required in this method. It is quite easy for EV, but not so for internal combustion vehicle (ICV). Proposed algorithm is examined with experimental results with laboratory-made electric vehicle “UOT Electric March-I”. These experimental data show the effectiveness of our new skid detection method. Another simulation results show that the skid prevention can be achieved with proposed skid detection method.
Estimation of transmission losses in a changing electric power industry

This thesis is concerned with the basic problem of computing how much power is lost in transferring across an electric power network, as the power is injected into particular nodes of the network that represent points of power supply and taken out at other nodes, that represent points of power consumption. This topic is revisited because of an increased tendency for economic transfers that go beyond the transfer levels at which transmission loss is relatively small, together with the questions of compensation for transmission losses in a deregulated/competitive power industry, including possible compensation of transmission losses at the end-user level, i.e., at the level of their cause. The question posed in this thesis is if the localized response of system voltages and angles to input changes could be used to: (i) locally estimate transmission losses caused by an end user, keeping in mind availability of real-time information networks, and, (ii) compensate for these losses by injecting the power corresponding to this estimated transmission loss, and not be dependent on the cost for this service dictated by someone else. Thesis Supervisor: Marija Ilid Title: Senior Research Scientist
Analysis of Driving Behaviour through Instrumented Vehicles

India ranks among the countries with the worst road safety record. With over 150,000 road fatalities in the country in 2019, safety is one of the top priorities of the government in the road sector. “Fault of Driver” was reported to be the cause of the accident in about 84% of the cases. This assumes significance in the context of the potential for technology applications in assisting and warning drivers for minimizing driving errors. This paper evaluates driving behavior under various scenarios of road conditions and traffic flow. Essentially, portable sensors were deployed in the vehicles to monitor the vehicle kinematics and assess the drivers' driving behavior. Test runs were conducted in Chennai city in cars mounted with fabricated Inertial Measurement Unit (IMU) interfaced with Real-Time Clock (RTC) and Arduino board along with GPS. Data on vehicle accelerations, decelerations, speeds, and jerk were collected for 44 trips (test runs) in the city. These data were synthesized and utilized to conduct detailed analyses of driving behavior. Acceleration noise, number of accelerations or decelerations per kilometer, speed drops, and jerk characteristics were analyzed in detail. Conclusions were drawn on various driving parameters of the drivers. Regression models relating jerk with various independent variables were built and validated. The drivers were classified into non-aggressive or aggressive driving styles with the help of k-means clustering. This study's findings can serve as useful insights in developing technology solutions in the form of dashboard signs, indicators, messages, warnings, etc. to alert and assist the driver, which will likely result in corrective driving behavior over time. The emergence of connected and autonomous vehicles in developed countries is making headline news these days. This research effort can be viewed as a precursor to such thrust areas, focusing on the safety dimensions.
Am I Being Bullied on Social Media? An Ensemble Approach to Categorize Cyberbullying

People can easily reveal their aggressive remarks on social media platforms using the anonymity it provides. During the COVID-19 pandemic, the usage of social media has been increased several times according to surveys and people are vulnerable to cyber attacks now more than ever. Prevention of cyberbullying needs careful monitoring and identification. Most of the existing works on cyberbullying detection employed traditional machine learning classifiers with handcrafted fea-tures, and deep learning-based models have made their way in this domain very recently. Categorizing cyberbullying based on traits is a complex task and needs contextual consideration. In this work, we have proposed a new approach to detect cyberbullying on social media platforms using a neural ensemble method of transformer-based architectures with attention mechanism. Our proposed architecture is trained on one balanced and one imbalanced dataset and outperforms the given ML and DNN baselines by a significant margin in both cases. We achieved an average F1-score of 95.59% for five classes and 90.65% for six classes on the Fine-Grained Cyberbullying Dataset (FGCD), and 87.28% on Twitter parsed dataset. Our in-depth results provide great insights into the effectiveness of transformer-based models in cyberbullying detection and paves the way for future researches to combat this serious online issue. We have released our models and code.1
Spot Cooling - Part 2: Recommendations for design of spot cooling systems

In this paper, the results of human subject and thermal manikin experiments with cooling jets are analyzed and discussed from the viewpoint of the design of spot-cooling systems. Large individual differences among people with regard to their physiological and subjective responses to the thermal environment and the selected preferred jet velocity make it unrealistic to propose one velocity for all persons depending on environmental conditions. A major conclusion of this study is that a spot-cooling system with individual control of the jet velocity and, impossible, control of the jet temperature is the most desirable. The spot cooling should provide a maximum local velocity of 4 meters per second (m/s) in the workplace. The jet outlet should be as close as possible to the person to provide energy-efficient cooling and maximum cooling benefit. The range of local jet air temperatures accepted by the subjects as a function of the room air temperature is given in the paper. A vertical jet has a stronger cooling ability and provides a more uniform cooling of the body than a horizontal jet. Nevertheless, the subjects were able to select an acceptable thermal environment with both vertical and horizontal jets. They found a compromise between decreasedmore » warm discomfort for the whole body and increased local draft discomfort or discomfort due to the pressure of the air movement. A combination of a vertical jet, a helmet, and a clothing ensemble of a design modified to protect the neck and shoulders from draft is recommended for use at room air temperatures above 35 C. « less
Chronic Respiratory Disease: Risk Modeling Potential and Limitations

Chronic Respiratory Diseases (CRDs), including Chronic Obstructive Pulmonary Disease (COPD) and asthma, are among the leading causes of mortality worldwide, with 545 million prevalent cases in 2017. Symptoms of noninfectious CRDs are often exacerbated by ambient air pollution and changes in temperature and humidity. This study explores a novel application of machine learning to forecast CRD risk and discuss its merits and limitations. We developed, trained, and tested a Random Forest regressor using datasets over the United States during 2000-2016, with mortality rate as the target variable. The final regressor produced R-squared values of 0.7526 and 0.7528 for cross-validation and test dataset prediction, respectively, implying that our model generalizes well out-of-sample. The selected features comprise location and temporal encoders, population density, and net primary production. The study reveals significant potential for modeling CRD risk but highlights setbacks due to the primarily noninfectious nature of CRDs, phenomena only identifiable on finer spatiotemporal scales, and data limitations. We also identify methods that may refine our approach and describe future developments that may improve CRD risk modeling.
Optical Energy Harvesting Materials

Optical energy harvesting signifies an integrated approach to the gathering of energy from light. The mechanisms that operate in the molecular apparatus of natural photosynthetic systems owe much of their efficiency to the highly localized pooling of energyâthough that is not a feature that has been emulated in most artificial constructs. Recently, however, biomimetic energy pooling has become a theme of significant interest in the arena of photoactive materials, where applications are less restricted by a direct focus on the efficiency of solar energy harvesting. These materials present more immediate opportunities for implementation in devices for signal processing, optical computing and information technology (IT) applications. Exploiting advances in synthetic chemistry and laser photophysics, new energy pooling materials are designed to operate at much higher than ambient intensities of light, with operational characteristics geared to specific laser input wavelengths. In this chapter, it is shown how key principles learned from nature are being applied and extended in the design of optically nonlinear materials for energy pooling. Particular attention is bestowed on dendrimers, other multichromophore arrays, and rare-earth-doped materials.
LinkExplorer: Predicting, explaining and exploring links in large biomedical knowledge graphs

Summary Machine learning algorithms for link prediction can be valuable tools for hypothesis generation. However, many current algorithms are black boxes or lack good user interfaces that could facilitate insight into why predictions are made. We present LinkExplorer, a software suite for predicting, explaining and exploring links in large biomedical knowledge graphs. LinkExplorer integrates our novel, rule-based link prediction engine SAFRAN, which was recently shown to outcompete other explainable algorithms and established black box algorithms. Here, we demonstrate highly competitive evaluation results of our algorithm on multiple large biomedical knowledge graphs, and release a web interface that allows for interactive and intuitive exploration of predicted links and their explanations. Availability and Implementation A publicly hosted instance, source code and further documentation can be found at https://github.com/OpenBioLink/Explorer. Contact matthias.samwald@meduniwien.ac. at Supplementary information Supplementary data are available at Bioinformatics online.
Graph-guided Bayesian SVM with Adaptive Structured Shrinkage Prior for High-dimensional Data

Support vector machine (SVM) is a popular classification method for the analysis of a wide range of data including big biomedical data. Many SVM methods with feature selection have been developed under the frequentist regularization or Bayesian shrinkage frameworks. On the other hand, the value of incorporating a priori known biological knowledge, such as those from functional genomics and functional proteomics, into statistical analysis of -omic data has been recognized in recent years. Such biological information is often represented by graphs. We propose a novel method that assigns Laplace priors to the regression coefficients and incorporates the underlying graph information via a hyper-prior for the shrinkage parameters in the Laplace priors. This enables smoothing of shrinkage parameters for connected variables in the graph and conditional independence between shrinkage parameters for disconnected variables. Extensive simulations demonstrate that our proposed methods achieve the best performance compared to the other existing SVM methods in terms of prediction accuracy. The proposed method are also illustrated in analysis of genomic data from cancer studies, demonstrating its advantage in generating biologically meaningful results and identifying potentially important features.
A Fine-grain Geospatial and Demographic Analysis of Breast Cancer Patterns in New York State

Cancer impacts many people across New York State, with breast cancer being one type of cancer that is especially prevalent. Breast cancer can be detected through screening procedures, and if caught early in its development, has a higher likelihood of treatability and a less severe outcome. In order to better understand existing patterns of breast cancer throughout New York State, we conducted a geospatial and demographic analysis in which we sought to identify disparities in screening, diagnosis, and mortality rates across regions and demographic groups. Statistical analyses were completed using patient-level data from the New York Statewide Planning and Research Cooperative System (SPARCS) from 2005 to 2019, where demographic data including age, race, and ethnicity, as well as location-based data such as addresses were collected. Geospatial analysis results revealed clustering for screening and diagnosis rates in certain regions of the state, as well as hot spots. Results from the demographic analysis indicated temporal trends in cancer rates for various age, race, and ethnic groups, as well as a disparity in the mortality rates of breast cancer across race and ethnic groups.
Scalable Sequence Clustering for Large-Scale Immune Repertoire Analysis

The development of the next-generation sequencing technology has enabled systems immunology researchers to conduct detailed immune repertoire analysis at the molecular level that allows researchers to understand the healthiness of a patient’s immune system. Recent studies have shown that the single-linkage clustering algorithm can give the best results for B cell clonality analysis – a critical type of immune repertoire sequencing (IR-Seq) analysis. Large sequence datasets (e.g., millions of sequences) are being collected to comprehensively understand how a specific person’s immune system evolves over different stages of disease development. However, the classical single-linkage clustering algorithm does not scale well to such large sequence datasets. Surprisingly, no study has been done to address this scalability issue for immunology research and development. We study three different strategies to scale up the single-linkage algorithm for sequence data. They include (1) the approximate single-linkage algorithm enhanced with the non-Euclidean indexing methods, (2) the Spark-based single-linkage algorithm (SparkMST) that was originally designed for vector data and now modified for sequence data, and (3) a new tree-based sequence summarization approach – SCT that aims to reduce the data for single-linkage clustering with well-preserved clustering quality.We have implemented these approaches and experimented with real sequence datasets for B cell clonality analysis. (1) The index-enhanced hierarchical clustering algorithm (e.g., VPT-HC using the Vantage-Point tree for indexing) preserves the clustering quality very well while significantly reducing the time complexity. (2) The SCT approach serving as a preprocessing step can effectively reduce data size for clustering. The overall clustering, SCT followed by VPT-HC, is the fastest among the evaluated single-machine algorithms. However, this approach also slightly affects the clustering quality. (3) The SparkMST parallel algorithm scales out nicely and also gives exact single-linkage clustering results. However, SparkMST is tied to the single-linkage algorithm and cannot be extended to general hierarchical clustering algorithms. Although this study focused on the specific application area: the B cell clonality analysis, we believe other sequence data analysis problems may find the developed scalable techniques useful.
Automatic ICD-10 Coding Based on Multi-Head Attention Mechanism and Gated Residual Network

Classifying diseases in electronic medical records into corresponding ICD codes requires not only a large amount of medical knowledge but also a large number of coders, which is time-consuming and labor-consuming. Therefore, automatic coding is of great significance. This paper aims to build a deep learning model for automatic ICD-10 coding from a batch of Chinese electronic medical records. The data enhancement, convolutional neural network, attention mechanism, and the gating residual network proposed by the author were used to code ICD code corresponding to the distribution of medical record information by supervised learning. The benchmark model and ablation model were tested on a data set of Chinese electronic medical records. The effectiveness of the proposed modules, such as feature aggregation, multi-head attention mechanism, dilated convolution, and gating residuals, was verified. In the automatic ICD coding task for 104 diseases, the accuracy of the proposed method is 91.71%, and the F1-Score is 92.11%. 1
Deployment and Management of Time Series Forecasts in Ocean Industry

Machine learning has not achieved the same degree of success in environmental applications as in other industries. Challenges around data sparsity, quality, and consistency have limited the impact of deep neural network approaches and restricted the focus to research applications. An alternative approach – that is more amenable to the characteristics of data coming from disparate IoT devices deployed at different times and locations in the ocean – is to develop many lightweight models that can be readily scaled up or down based on the number of devices available at any time. This paper presents a serverless framework that naturally marries a single IoT sensor device with a forecasting model. Aspects related to data ingestion, data processing, model training and deployment are described. The framework is applied to a fish farm site in Atlantic Canada.
Efficient and Privacy-Preserving Collaborative Intrusion Detection Using Additive Secret Sharing and Differential Privacy

Intrusion Detection Systems are commonly used by organizations to monitor network traffic and detect attacks or suspicious behaviours. However, many attacks occur across organizations and are often difficult to detect using any single IDS. Collaborative Intrusion Detection Systems could lead to more accurate prediction and detection of cyber threats as well as a reduction of security administrators’ workload as similar threats from different places can be merged. However, most organizations are unwilling to disclose sensitive information about their internal network topology and traffic, lending these systems unusable. Existing solutions using homomorphic encryption and secure multi-party computation are often expensive. In this paper, we propose efficient and privacy preserving techniques to correlate alerts generated at different organizations. We propose skPrototypes, a distributed clustering algorithm for horizontally partitioned mixed data using additive secret sharing. This algorithm can be used to create a privacy preserving, collaborative intrusion detection system. We also propose dpkPrototypes which uses differential privacy on categorical attributes and is more efficient than skPrototypes for categorical attributes with many distinct values. Theoretical and experimental results validate the effectiveness of our algorithms.