Computing has undergone a significant transformation over the past two decades, shifting from a machine-based approach to a human-centric, virtually invisible service known as ubiquitous or pervasive computing. This change has been achieved by incorporating small embedded devices into a larger computational system, connected through networking and referred to as edge devices. When these devices are also connected to the Internet, they are generally named Internet-of-Thing (IoT) devices. Developing Machine Learning (ML) algorithms on these types of devices allows them to provide Artificial Intelligence (AI) inference functions such as computer vision, pattern recognition, etc. However, this capability is severely limited by the device’s resource scarcity. Embedded devices have limited computational and power resources available while they must maintain a high degree of autonomy. While there are several published studies that address the computational weakness of these small systems-mostly through optimization and compression of neural networks- they often neglect the power consumption and efficiency implications of these techniques. This study presents power efficiency experimental results from the application of well-known and proven optimization methods using a set of well-known ML models. The results are presented in a meaningful manner considering the “real world” functionality of devices and the provided results are compared with the basic “idle” power consumption of each of the selected systems. Two different systems with completely different architectures and capabilities were used providing us with results that led to interesting conclusions related to the power efficiency of each architecture.

With the advent of Information Technology in the medical world, various radiological modalities produce a variety of digital medical files most often datasets and images. These files as any digital asset should be protected from unwanted modification of their contents, especially as they contain vital medical information. Thus their protection and authentication seems of great importance and this need will rise along with the future standardization of exchange of data between hospitals or between patients and doctors. Watermarking, a technique first introduced for multimedia files, provides a method for authentication and protection and has been recently applied to medical images. In this paper, we propose a novel watermarking technique where the region of non-interest (RONI) of medical Magnetic Resonance Imaging (MRI) images, is used to embed the region of interest (ROI). In this way, any tampering attempt, not only will be detected, but also the image could be self-restored, back to its previous, ldquooriginalrdquo form by extracting the ROI from the RONI.

Having as a main objective the exploration of power efficiency of microcontrollers running machine learning models, this manuscript contrasts the performance of two types of state-of-the-art microcontrollers, namely ESP32 with an LX6 core and ESP32-S3 with an LX7 core, focusing on the impact of process acceleration technologies like cache memory and vectoring. The research employs experimental methods, where identical machine learning models are run on both microcontrollers under varying conditions, with particular attention to cache optimization and vector instruction utilization. Results indicate a notable difference in power efficiency between the two microcontrollers, directly linked to their respective process acceleration capabilities. The study concludes that while both microcontrollers show efficacy in running machine learning models, ESP32-S3 with an LX7 core demonstrates superior power efficiency, attributable to its advanced vector instruction set and optimized cache memory usage. These findings provide valuable insights for the design of power-efficient embedded systems supporting machine learning for a variety of applications, including IoT and wearable devices, ambient intelligence, and edge computing and pave the way for future research in optimizing machine learning models for low-power, embedded environments.

We aimed to clarify the emerging epigenetic landscape in a group of genes classified as “modifier genes” of the β-type globin genes (HBB cluster), known to operate in trans to accomplish the two natural developmental switches in globin expression, from embryonic to fetal during the first trimester of conception and from fetal to adult around the time of birth. The epigenetic alterations were determined in adult sickle cell anemia (SCA) homozygotes and SCA/β-thalassemia compound heterozygotes of Greek origin, who are under hydroxyurea (HU) treatment. Patients were distinguished in HU responders and HU non-responders (those not benefited from the HU) and both, and in vivo and in vitro approaches were implemented. We examined the CpG islands’ DNA methylation profile of BCL11A, KLF1, MYB, MAP3K5, SIN3A, ZBTB7A, and GATA2, along with γ-globin and LRF/ZBTB7A expression levels. In vitro treatment of hematopoietic stem cells (HSCs) with HU induced a significant DNA hypomethylation pattern in ZBTB7A (p*, 0.04) and GATA2 (p*, 0.03) CpGs exclusively in the HU non-responders. Also, this group of patients exhibited significantly elevated baseline methylation patterns in ZBTB7A, before the HU treatment, compared to HU responders (p*, 0.019) and to control group of healthy individuals (p*, 0.021), which resembles a potential epigenetic barrier for the γ-globin expression. γ-Globin expression in vitro matched with detected HbF levels during patients’ monitoring tests (in vivo) under HU treatment, implying a good reproducibility of the in vitro HU epigenetic effect. LRF/ZBTB7A expression was elevated only in the HU non-responders under the influence of HU. This is one of the very first pharmacoepigenomic studies indicating that the hypomethylation of ZBTB7A during HU treatment enhances the LRF expression, which by its turn suppresses the HbF resumption in the HU non-responders. Its role as an epigenetic regulator of hemoglobin switching is also supported by the wide distribution of ZBTB7A-binding sites within the 5′ CpG sequences of all studied human HBB cluster “modifier genes.” Also, the baseline methylation level of selective CpGs in ZBTB7A and GATA2 could be an indicator of the negative HU response among the β-type hemoglobinopathy patients.

Background: The aim of this study was to determine the genotype distribution and allelic frequencies of ACE (I/D), AGTR1 (A +1166 C), BDKRB2 (+9/−9) and LEP (G–2548A) genomic variations in 175 Greek athletes who excelled at a national and/or international level and 169 healthy Greek adults to identify whether some particular combinations of these loci might serve as predictive markers for superior physical condition. Results: The D/D genotype of the ACE gene (p = 0.034) combined with the simultaneous existence of BDKRB2 (+9/−9) (p = 0.001) or LEP (G/A) (p = 0.021) genotypes was the most prevalent among female athletes compared to female controls. A statistical trend was also observed in BDKRB2 (+9/−9) and LEP (G–2548A) heterozygous genotypes among male and female Greek athletes, and in ACE (I/D) only in male athletes. Finally, both male and female athletes showed the highest rates in the AGTR1 (A/A) genotype. Conclusions: Our results suggest that the co-existence of ACE (D/D), BDKRB2 (+9/−9) or LEP (G/A) genotypes in female athletes might be correlated with a superior level of physical performance.