Singlet-Singlet and Triplet-Triplet Energy Transfer in Bichromophoric Cyclic Peptides

Intramolecular singlet-singlet (SSET) and triplet-triplet (TTET) energy transfer have been studied in two cyclic octapeptides, 1A and 2A, and their open chain analogs, 1B and 2B. The peptides are constructed by a solid phase synthetic technique from enantiomerically pure amino acids with alternating chirality. Cyclic peptides with this arrangement of amino acids preferentially adopt flat, disk-like conformations where the peptide side chains lie on the outside of the ensemble. In 1A, benzophenone and naphthalene chromophores are incorporated as 4-benzoyl-L-phenylalanine and 2-naphtyl-L-alanine at positions 1 and 5 in the peptide sequence while in 2A, these chromophores occupy positions 1 and 3. Molecular modeling studies indicate that the interchromophore separation is larger in 1A than in 2A. This difference in separation is apparent from the observation of TTET energy transfer in 2A, which is consistent with the short range nature of TTET. Low temperature phosphorescence results indicate that intramolecular TTET is efficient in 2A and 2B and occurs with a rate of kTTET \u3e 9.4x103 s-1. Intramolecular SSET occurs efficiently within these cyclic and open chain peptides. 1A undergoes intramolecular SSET from the naphthalene chromophore to the benzophenone chromophore with kSSET \u3e 3.7x107 s-1, while in 2A with kSSET \u3e3.0x107 s-1. Results obtained by modeling, UV-Visible spectroscopy, fluorescence and phosphorescence spectroscopies and transient absorption experiments are described

Role of Artificial Intelligence in the Internet of Things (IoT) Cybersecurity

In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these three topics with the purpose of comprehensively presenting and summarizing relevant literature in these fields

Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated into applications throughout all network layers. However, the security concerns on network functions of NextG using AI-based models, i.e., model poisoning, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cybersecurity threats, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB\u27s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while mitigation methods can make models more robust against adversarial attacks. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack. The results indicate that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks

A Lightweight App Distribution Strategy to Generate Interest in Complex Commercial Apps: Case Study of an Automated Wound Measurement System

Tablet-based healthcare technologies automating clinical triage procedures hold exciting promise for increased precision and expediency. These point-of-care (POC) solutions are often complex, and their introduction to the marketplace may encounter cost and usability barriers. One example triage procedure is wound measurement. This paper demonstrates an innovative approach to POC wound measurement by introducing a free “light” version of a wound measurement mobile app that serves as a teaser for a full-featured commercial offering. We first describe the commercial offering; a 3D wound assessment tablet application. Then we present the smartphone app that inherits features from the tablet app. The smartphone app adopts a simple scaling algorithm to address the lack of a highly advanced computer vision system for the automated wound measurement task that exists in the tablet app. This paper describes the design process for developing this smartphone app, provides a detailed exposition of the scaling algorithm, and discusses the significance of this approach to app development and distribution

Simultaneous Wound Border Segmentation and Tissue Classification Using a Conditional Generative Adversarial Network

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs

WG\u3csup\u3e2\u3c/sup\u3eAN: Synthetic Wound Image Generation Using Generative Adversarial Network

In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various sizes taken from real hospital environments. Hyperparameters such as epoch count and dataset size for training tasks are studied to find optimum training conditions as well. The performance of the developed model was evaluated through a mean squared error (MSE) metric to determine the similarity between generated and actual wounds. Visual inspection is performed to examine generated wound images. The results show that the proposed synthetic wound image generation (WG2AN) model has great potential to be used in medical training and performs well in producing synthetic wound images with a 1000-image training dataset and 200 epochs of training

Hiccup Due to Aripiprazole Plus Methylphenidate Treatment in an Adolescent with Attention Deficit and Hyperactivity Disorder and Conduct Disorder: A Case Report

Our case had hiccups arising in an adolescent with the attention deficit and hyperactivity disorder (ADHD) and conduct disorder (CD) after adding aripiprazole treatment to extended-release methylphenidate. Actually, antipsychotics are also used in the treatment of hiccups, but studies suggest that they can cause hiccups as well. Within 12 hours of taking 2.5 mg aripiprazole added to extended-release methylphenidate at a dose of 54 mg/day, 16-year-old boy began having hiccups in the morning, which lasted after 3-4 hours. As a result, aripiprazole was discontinued and methylphenidate was continued alone because we could not convince the patient to use another additional drug due to this side effect. Subsequently, when his behavior got worsened day by day, his mother administered aripiprazole alone again at the dose of 2.5 mg/day at the weekend and continued treatment because hiccup did not occur again. But when it was administered with methylphenidate on Monday, hiccup started again next morning and lasted one hour at this time. In conclusion, we concluded that concurrent use of methylphenidate and aripiprazole in this adolescent led to hiccups

Roles of the systemic inflammatory response biomarkers in the diagnosis of cancer patients with solid tumors

Aim: Cancer is still considered as one of the leading causes of mortality worldwide. Various tumor factors have been used for the diagnosis and follow-up of solid tumors; however, their clinical features remains controversial in terms of their diagnostic, prognostic, and predictive values. In this study, we aimed to investigate the use of the systemic inflammatory response biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), in the diagnosis of solid tumors. Method: We retrospectively analyzed the records of 256 patients with solid tumors, including lung, breast, liver, and pancreatic cancers, who were diagnosed at the outpatient clinics of our institution between January 2017 and July 2018. The neutrophil, lymphocyte, monocyte, and platelet counts were measured using a hematology analyzer and the results were analyzed statistically. Results: The results of the receiver operating characteristic analysis showed that the NLR and LMR could be statistically reliable biomarkers, with area under the curve (AUC) values of 0.574 (p = 0.017) and 0.596 (p = 0.002). However, the PLR statistically failed to discriminate the patients and the control subjects, with AUC values of 0.545 (p = 0.148). Conclusions: Certain systemic inflammatory response biomarkers, such as the NLR and LMR, can play roles in the clinical diagnosis of patients with solid tumors

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation to extract additional knowledge that can also be interpreted by non-data-science experts, such as medical scientists and physicians. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes

Defensive Distillation-based Adversarial Attack Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated into applications throughout all network layers. However, the security concerns on network functions of NextG using AI-based models, i.e., model poisoning, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cybersecurity threats, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while mitigation methods can make models more robust against adversarial attacks. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack. The results indicate that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.publishedVersio