Drug Repurposing for Cancers With Limited Survival: Protocol for a Retrospective Cohort Study.

BACKGROUND: Only 5% of the molecules tested in oncology phase 1 trials reach the market after an average of 7.5 years of waiting and at a cost of tens of millions of dollars. To reduce the cost and shorten the time of discovery of new treatments, "drug repurposing" (research with molecules already approved for another indication) and the use of secondary data (not collected for the purpose of research) have been proposed. Due to advances in informatics in clinical care, secondary data can, in some cases, be of equal quality to primary data generated through prospective studies. OBJECTIVE: The objective of this study is to identify drugs currently marketed for other indications that may have an effect on the prognosis of patients with cancer. METHODS: We plan to monitor a cohort of patients with high-lethality cancers treated in the public health system of Catalonia between 2006 and 2012, retrospectively, for survival for 5 years after diagnosis or until death. A control cohort, comprising people without cancer, will also be retrospectively monitored for 5 years. The following study variables will be extracted from different population databases: type of cancer (patients with cancer cohort), date and cause of death, pharmacological treatment, sex, age, and place of residence. During the first stage of statistical analysis of the patients with cancer cohort, the drugs consumed by the long-term survivors (alive at 5 years) will be compared with those consumed by nonsurvivors. In the second stage, the survival associated with the consumption of each relevant drug will be analyzed. For the analyses, groups will be matched for potentially confounding variables, and multivariate analyses will be performed to adjust for residual confounding variables if necessary. The control cohort will be used to verify whether the associations found are exclusive to patients with cancer or whether they also occur in patients without cancer. RESULTS: We anticipate discovering multiple significant associations between commonly used drugs and the survival outcomes of patients with cancer. We expect to publish the initial results in the first half of 2024. CONCLUSIONS: This retrospective study may identify several commonly used drugs as candidates for repurposing in the treatment of various cancers. All analyses are considered exploratory; therefore, the results will have to be confirmed in subsequent clinical trials. However, the results of this study may accelerate drug discovery in oncology. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48925.

Two decades of assistive technologies to empower people with disability: a systematic mapping study.

PURPOSE: Information and Communication Technologies have transformed our lives in different social areas, facilitating interpersonal relationships thanks to technological tools. In the specific case of people with disabilities, Assistive Technologies (ATs) break down barriers and increase opportunities to become active members of society with equal opportunities. MATERIALS AND METHODS: This paper presents a systematic mapping study that analyzes the current state-of-the-art of ATs proposed in the literature to support the empowering of people with disability. Specifically, this paper focuses on (1) describing a global vision of the scientific literature published in the last 20 years about ATs in the computer science field and (2) identifying research needs, gaps, and trends. RESULTS: For this purpose, an in-depth analysis of 389 primary studies is presented. The information obtained from the mapping process is also constrained. Concretely, 35 ATs versus 22 disabilities are compared, obtaining striking peaks for some disabilities described in the discussion. CONCLUSIONS: Finally, the findings show that several areas have been covered only lightly, revealing interesting future directions and challenges for junior researchers.

• ATs have the potential to break down barriers for people with disabilities, enabling them to participate more fully in society. This implies a need for rehabilitation programs to incorporate ATs into their strategies to enhance social inclusion.• Given the transformative role of ICT, rehabilitation programs should focus on helping people with disabilities develop the necessary technological skills to utilize ATs effectively.• This work highlights the diversity of ATs and disabilities, suggesting a need for personalized rehabilitation plans that match specific ATs to individual disabilities.• Rehabilitation professionals should be trained to assess and recommend appropriate ATs for each case. Rehabilitation programs should consider incorporating cutting-edge ATs and staying involved in research to contribute to future developments to cover gaps and challenges identified.

Conditioned Cooperative training for semi-supervised weapon detection.

Violent assaults and homicides occur daily, and the number of victims of mass shootings increases every year. However, this number can be reduced with the help of Closed Circuit Television (CCTV) and weapon detection models, as generic object detectors have become increasingly accurate with more data for training. We present a new semi-supervised learning methodology based on conditioned cooperative student-teacher training with optimal pseudo-label generation using a novel confidence threshold search method and improving both models by conditional knowledge transfer. Furthermore, a novel firearms image dataset of 458,599 images was collected using Instagram hashtags to evaluate our approach and compare the improvements obtained using a specific unsupervised dataset instead of a general one such as ImageNet. We compared our methodology with supervised, semi-supervised and self-supervised learning techniques, outperforming approaches such as YOLOv5 m (up to ＋19.86), YOLOv5l (up to ＋6.52) Unbiased Teacher (up to ＋10.5 AP), DETReg (up to ＋2.8 AP) and UP-DETR (up to ＋1.22 AP).

CrimeNet: Neural Structured Learning using Vision Transformer for violence detection.

The state of the art in violence detection in videos has improved in recent years thanks to deep learning models, but it is still below 90% of average precision in the most complex datasets, which may pose a problem of frequent false alarms in video surveillance environments and may cause security guards to disable the artificial intelligence system. In this study, we propose a new neural network based on Vision Transformer (ViT) and Neural Structured Learning (NSL) with adversarial training. This network, called CrimeNet, outperforms previous works by a large margin and reduces practically to zero the false positives. Our tests on the four most challenging violence-related datasets (binary and multi-class) show the effectiveness of CrimeNet, improving the state of the art from 9.4 to 22.17 percentage points in ROC AUC depending on the dataset. In addition, we present a generalisation study on our model by training and testing it on different datasets. The obtained results show that CrimeNet improves over competing methods with a gain of between 12.39 and 25.22 percentage points, showing remarkable robustness.

Real-time gun detection in CCTV: An open problem.

Object detectors have improved in recent years, obtaining better results and faster inference time. However, small object detection is still a problem that has not yet a definitive solution. The autonomous weapons detection on Closed-circuit television (CCTV) has been studied recently, being extremely useful in the field of security, counter-terrorism, and risk mitigation. This article presents a new dataset obtained from a real CCTV installed in a university and the generation of synthetic images, to which Faster R-CNN was applied using Feature Pyramid Network with ResNet-50 resulting in a weapon detection model able to be used in quasi real-time CCTV (90 ms of inference time with an NVIDIA GeForce GTX-1080Ti card) improving the state of the art on weapon detection in a two stages training. In this work, an exhaustive experimental study of the detector with these datasets was performed, showing the impact of synthetic datasets on the training of weapons detection systems, as well as the main limitations that these systems present nowadays. The generated synthetic dataset and the real CCTV dataset are available to the whole research community.

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things.

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.

Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets.

BACKGROUND: In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works. METHODS: By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad. RESULTS: The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper. CONCLUSIONS: This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.