Because of the high prevalence and associated suffering, disability and economic burden of painful DPN, it is important that diabetic patients are periodically screened, using a simple instrument such as the DN4, and receive appropriate treatment if symptoms develop.

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of lowbitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

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A reliable, in vivo, rat model of seminal vesicle organ compliance and contractility is described. The seminal vesicle is a highly contractile, compliant smooth muscular organ with dynamic properties analogous to that of the urinary bladder. This experimental system may allow for the investigation of pharmacologic and other physiological influences on in vivo organ activity.

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In a prospective study, the polymorphism of oestrogen receptor β gene was investigated in nonobstructive azoospermia patients. Ninety infertile patients with nonobstructive azoospermia diagnosed after two semen analysis, 2 weeks apart and negative testicular sperm extraction during intracytoplasmic sperm injection, and 60 fertile men as controls were enrolled in the study. Semen analysis, hormonal profile and allele-specific PCR reaction were performed to detect variants of the RsaI polymorphism of the oestrogen receptor β gene for all patients and controls. The mean patient's age was significantly lower than the mean age of the controls (P < 0.05). There was a significant increase in the mean serum levels of FSH, LH, free testosterone and E2 and significant decrease in total testosterone in patients than controls (P < 0.05). In the patients, the frequency of the homozygous GG, heterozygous AG and homozygous AA genotype was 83.3%, 14.3% and 3.3% respectively, whereas their frequencies in the controls were 95%, 5% and 0% respectively (odds ratio 3.8). There is no significant correlation between ERß polymorphisms and patient's age or pituitary and sex hormones (P > 0.05). Our findings suggested that in Egyptian population, genetic mutation in ERß is associated with the risk of nonobstructive azoospermia.

Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task, they are usually not optimized even for common smartphone AI hardware, not to mention more constrained smart TV platforms that are often supporting INT8 inference only. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a real-time performance on mobile or edge NPUs. For this, the participants were provided with the DIV2K dataset and trained quantized models to do an efficient 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated NPU capable of accelerating quantized neural networks. The proposed solutions are fully compatible with all major mobile AI accelerators and are capable of reconstructing Full HD images under 40-60 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.