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General General

A Genetic Programming-Based Low-Level Instructions Robot for Realtimebattle.

In Entropy (Basel, Switzerland)

RealTimeBattle is an environment in which robots controlled by programs fight each other. Programs control the simulated robots using low-level messages (e.g., turn radar, accelerate). Unlike other tools like Robocode, each of these robots can be developed using different programming languages. Our purpose is to generate, without human programming or other intervention, a robot that is highly competitive in RealTimeBattle. To that end, we implemented an Evolutionary Computation technique: Genetic Programming. The robot controllers created in the course of the experiments exhibit several different and effective combat strategies such as avoidance, sniping, encircling and shooting. To further improve their performance, we propose a function-set that includes short-term memory mechanisms, which allowed us to evolve a robot that is superior to all of the rivals used for its training. The robot was also tested in a bout with the winner of the previous "RealTimeBattle Championship," which it won. Finally, our robot was tested in a multi-robot battle arena, with five simultaneous opponents, and obtained the best results among the contenders.

Romero Juan, Santos Antonino, Carballal Adrian, Rodiguez-Fernandez Nereida, Santos Iria, Torrente-Patiño Alvaro, Machado Juan Tuñas And Penousal


RealTimeBattle, artificial intelligence, creative computation, evolutionary game, evolutionary robotics, genetic programming, robots

General General

Development and Validation of an Automated Video Tracking Model for Stabled Horses.

In Animals : an open access journal from MDPI

Changes in behaviour are often caused by painful conditions. Therefore, the assessment of behaviour is important for the recognition of pain, but also for the assessment of quality of life. Automated detection of movement and the behaviour of a horse in the box stall should represent a significant advancement. In this study, videos of horses in an animal hospital were recorded using an action camera and a time-lapse mode. These videos were processed using the convolutional neural network Loopy for automated prediction of body parts. Development of the model was carried out in several steps, including annotation of the key points, training of the network to generate the model and checking the model for its accuracy. The key points nose, withers and tail are detected with a sensitivity of more than 80% and an error rate between 2 and 7%, depending on the key point. By means of a case study, the possibility of further analysis with the acquired data was investigated. The results will significantly improve the pain recognition of horses and will help to develop algorithms for the automated recognition of behaviour using machine learning.

Kil Nuray, Ertelt Katrin, Auer Ulrike


automated video tracking, equine behaviour, image processing, machine learning, pain assessment

General General

Discrimination of sunflower seeds using multispectral and texture dataset in combination with region selection and supervised classification methods.

In Chaos (Woodbury, N.Y.)

The purpose of this study is to discriminate sunflower seeds with the help of a dataset having spectral and textural features. The production of crop based on seed purity and quality other hand sunflower seed used for oil content worldwide. In this regard, the foundation of a dataset categorizes sunflower seed varieties (Syngenta CG, HS360, S278, HS30, Armani, and High Sun 33), which were acquired from the agricultural farms of The Islamia University of Bahawalpur, Pakistan, into six classes. For preprocessing, a new region-oriented seed-based segmentation was deployed for the automatic selection of regions and extraction of 53 multi-features from each region, while 11 optimized fused multi-features were selected using the chi-square feature selection technique. For discrimination, four supervised classifiers, namely, deep learning J4, support vector machine, random committee, and Bayes net, were employed to optimize the multi-feature dataset. We observe very promising accuracies of 98.2%, 97.5%, 96.6%, and 94.8%, respectively, when the size of a region is (180 × 180).

Bantan Rashad A R, Ali Aqib, Naeem Samreen, Jamal Farrukh, Elgarhy Mohammed, Chesneau Christophe


General General

Visualization of Autophagy Progression by a Red-Green-Blue Autophagy Sensor.

In ACS sensors

Autophagy is a major degradation process of cytosolic components and misfolded proteins that is crucial for cellular homeostasis and for the pathogenesis of diverse diseases. Autophagy is initiated by the formation of phagophores, which mature to autophagosomes. The autophagosomes then fuse to lysosomes to form autolysosomes. Different stages of autophagy can be deregulated to cause autophagy-related diseases, and thus, an accurate detection of each stage of autophagy progression is critical for efficient therapeutic strategies for these diseases. To identify the different stages of autophagy progression, here, we developed a new autophagy flux sensor, named red-green-blue-LC3 (RGB-LC3). RGB-LC3 is composed of LC3 and red-green-blue (RGB) fluorescent proteins, which were carefully chosen by considering their separate spectral profiles, stability, brightness, and most importantly different pH sensitivities. Utilizing this RGB-LC3 and the predicted pH, we could clearly identify phagophores, autophagosomes, fusion stage, early autolysosomes, and mature autolysosomes in live cells. Furthermore, the RGB-LC3 sensor was successfully applied to distinguish different effects of Aβ monomers and oligomers on autophagy flux. Therefore, we developed a new autophagy flux sensor, RGB-LC3, which may be a valuable tool to further investigate the molecular mechanisms of autophagy and to develop efficient therapeutic strategies for autophagy-related diseases.

Kim Heejung, Kim Hyunbin, Choi Jaesik, Inn Kyung-Soo, Seong Jihye


RGB-LC3, autophagic flux, autophagy progression, fluorescent sensor, pH ratiometric sensor

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Artificial Intelligence Tools for Refining Lung Cancer Screening.

In Journal of clinical medicine

Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.

Espinoza J Luis, Dong Le Thanh


artificial intelligence and lung cancer, computers assisted diagnosis, early cancer diagnosis, lung cancer imaging, lung cancer screening

General General

A Customizable Analysis Flow in Integrative Multi-Omics.

In Biomolecules

The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements-four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based-to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.

Lancaster Samuel M, Sanghi Akshay, Wu Si, Snyder Michael P


analysis flow, bioinformatics, machine learning, multi-omics, multi-omics analysis, study design