QUANTIFICATION OF RESIDUAL CLOVE OIL, BENZOCAINE AND TRICAINE IN FISH FILLETS USING SPE AND UPLC-DAD

Residual quantification of the anesthetics clove oil (CO) – isoeugenol (ISO), eugenol (EUG) and methyleugenol (MET) –,benzocaine (BZN) and tricaine (MS-222) was made in fillets of two fish species: Nile tilapia (Oreochromis niloticus) and acatfish hybrid, cachadia (Pseudoplatystoma reticulatum x Leiarius marmoratus). Samples (n=4) of each fish wereevaluated after submitted to anesthesia in five dosages defined based on the induction time of each species afterdepuration times (0h, 12h, 24h and 48h). Different methodologies of sample preparation were tested and selectedaccording to the better recovery. The quantification of anesthetics was performed by UPLC-DAD. The variance of residualmeans among anesthetics, dosages and fish species was compared. After anesthesia (0h) both species, tilapia andcachadia, presented residual anesthetics. Fishes depurated during 12h, 24h and 48h did not present detectable values, itmeans, values were below the limits of detection. BZN presented the highest mean residual concentration for tilapia andcachadia (p=0.01), while MS-222 presented the lowest residual amounts in tilapias and EUG in cachadias, what may berelated to the metabolism and carcass composition of each fish species. There were no significant differences among thefive dosages, except the lowest MS-222 concentration in tilapias that resulted in higher residual concentrations becauselow dosages increase the induction time and consequently the permanence of the fish in anesthesia. Ultimately, meanvalues of residues in cachadia were higher than in tilapia, and MS-222 and EUG presented the lowest residual values fortilapia and cachadia, respectively

ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge", there is now a demand for even more efficient models. In order to meet the constraints of ultra-low-energy devices, we propose ULEEN, a model architecture based on weightless neural networks. Weightless neural networks (WNNs) are a class of neural model which use table lookups, not arithmetic, to perform computation. The elimination of energy-intensive arithmetic operations makes WNNs theoretically well suited for edge inference; however, they have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by BNNs to make significant strides in improving accuracy and reducing model size. We compare FPGA and ASIC implementations of an inference accelerator for ULEEN against edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we demonstrate classification on the MNIST dataset at 14.3 million inferences per second (13 million inferences/Joule) with 0.21 $\mu$s latency and 96.2% accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69 million inferences/Joule) with 0.31 $\mu$s latency and 95.83% accuracy. In a 45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million inferences/second at 98.46% accuracy, while a quantized Bit Fusion model achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy. In our search for ever more efficient edge devices, ULEEN shows that WNNs are deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from arXiv:2203.01479, most notably sections 5E and 5F.

Variability of systemic and oro-dental phenotype in two families with non-lethal Raine syndrome with FAM20C mutations

Background: Raine syndrome (RS) is a rare autosomal recessive bone dysplasia typified by osteosclerosis and dysmorphic facies due to FAM20C mutations. Initially reported as lethal in infancy, survival is possible into adulthood. We describe the molecular analysis and clinical phenotypes of five individuals from two consanguineous Brazilian families with attenuated Raine Syndrome with previously unreported features. Methods: The medical and dental clinical records were reviewed. Extracted deciduous and permanent teeth as well as oral soft tissues were analysed. Whole exome sequencing was undertaken and FAM20C cDNA sequenced in family 1. Results: Family 1 included 3 siblings with hypoplastic Amelogenesis Imperfecta (AI) (inherited abnormal dental enamel formation). Mild facial dysmorphism was noted in the absence of other obvious skeletal or growth abnormalities. A mild hypophosphataemia and soft tissue ectopic mineralization were present. A homozygous FAM20C donor splice site mutation (c.784 + 5 g > c) was identified which led to abnormal cDNA sequence. Family 2 included 2 siblings with hypoplastic AI and tooth dentine abnormalities as part of a more obvious syndrome with facial dysmorphism. There was hypophosphataemia, soft tissue ectopic mineralization, but no osteosclerosis. A homozygous missense mutation in FAM20C (c.1487C > T; p.P496L) was identified. Conclusions: The clinical phenotype of non-lethal Raine Syndrome is more variable, including between affected siblings, than previously described and an adverse impact on bone growth and health may not be a prominent feature. By contrast, a profound failure of dental enamel formation leading to a distinctive hypoplastic AI in all teeth should alert clinicians to the possibility of FAM20C mutations