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Mucolipidosis II (ML II) or inclusion cell disease (I-cell disease) is a rarely occurring autosomal recessive lysosomal enzyme-targeting disease. This disease is usually found to occur in individuals aged between 6 and 12 months, with a clinical phenotype resembling that of Hurler syndrome and radiological findings resembling those of dysostosis multiplex. However, we encountered a rare case of an infant with ML II who presented with prenatal skeletal dysplasia and typical clinical features of severe secondary hyperparathyroidism at birth. A female infant was born at 37+1 weeks of gestation with a birth weight of 1,690 g (<3rd percentile). Prenatal ultrasonographic findings revealed intrauterine growth retardation and skeletal dysplasia. At birth, the patient had characteristic features of ML II, and skeletal radiographs revealed dysostosis multiplex, similar to rickets. In addition, the patient had high levels of alkaline phosphatase and parathyroid hormone, consistent with severe secondary neonatal hyperparathyroidism. The activities of β-D-hexosaminidase and α-N-acetylglucosaminidase were moderately decreased in the leukocytes but were 5- to 10-fold higher in the plasma. Examination of a placental biopsy specimen showed foamy vacuolar changes in trophoblasts and syncytiotrophoblasts. The diagnosis of ML II was confirmed via GNPTAB genetic testing, which revealed compound heterozygosity of c.3091C>T (p.Arg1031X) and c.3456_3459dupCAAC (p.Ile1154GlnfsX3), the latter being a novel mutation. The infant was treated with vitamin D supplements but expired because of asphyxia at the age of 2 months.

Adult neurogenesis, the generation of new neurons throughout life, occurs in the subventricular zone of the dentate gyrus in the human hippocampal formation. It has been shown in rodents that adult hippocampal neurogenesis is needed for pattern separation, the ability to differentially encode small changes derived from similar inputs, and recognition memory, as well as the ability to recognize previously encountered stimuli. Improved hippocampus-dependent cognition and cellular readouts of adult hippocampal neurogenesis have been reported in daily energy restricted and intermittent fasting adult mice. Evidence that nutrition can significantly affect brain structure and function is increasing substantially. This randomized intervention study investigated the effects of intermittent and continuous energy restriction on human hippocampal neurogenesis-related cognition, which has not been reported previously. Pattern separation and recognition memory were measured in 43 individuals with central obesity aged 35–75 years, before and after a four-week dietary intervention using the mnemonic similarity task. Both groups significantly improved pattern separation (P = 0.0005), but only the intermittent energy restriction group had a significant deterioration in recognition memory. There were no significant differences in cognitive improvement between the two diets. This is the first human study to investigate the association between energy restriction with neurogenesis-associated cognitive function. Energy restriction may enhance hippocampus-dependent memory and could benefit those in an ageing population with declining cognition. This study was registered on ClinicalTrials.gov (NCT02679989) on 11 February 2016.

The bird's-eye-view (BEV) representation allows robust learning of multiple tasks for autonomous driving including road layout estimation and 3D object detection. However, contemporary methods for unified road layout estimation and 3D object detection rarely handle the class imbalance of the training dataset and multi-class learning to reduce the total number of networks required. To overcome these limitations, we propose a unified model for road layout estimation and 3D object detection inspired by the transformer architecture and the CycleGAN learning framework. The proposed model deals with the performance degradation due to the class imbalance of the dataset utilizing the focal loss and the proposed dual cycle loss. Moreover, we set up extensive learning scenarios to study the effect of multi-class learning for road layout estimation in various situations. To verify the effectiveness of the proposed model and the learning scheme, we conduct a thorough ablation study and a comparative study. The experiment results attest the effectiveness of our model; we achieve state-of-the-art performance in both the road layout estimation and 3D object detection tasks.

Reinforcement learning has shown an outstanding performance in the applications of games, particularly in Atari games as well as Go. Based on these successful examples, we attempt to apply one of the well-known reinforcement learning algorithms, Deep Q-Network, to the AI Soccer game. AI Soccer is a 5:5 robot soccer game where each participant develops an algorithm that controls five robots in a team to defeat the opponent participant. Deep Q-Network is designed to implement our original rewards, the state space, and the action space to train each agent so that it can take proper actions in different situations during the game. Our algorithm was able to successfully train the agents, and its performance was preliminarily proven through the mini-competition against 10 teams wishing to take part in the AI Soccer international competition. The competition was organized by the AI World Cup committee, in conjunction with the WCG 2019 Xi'an AI Masters. With our algorithm, we got the achievement of advancing to the round of 16 in this international competition with 130 teams from 39 countries.

There have been attempts to detect 3D objects by fusion of stereo camera images and LiDAR sensor data or using LiDAR for pre-training and only monocular images for testing, but there have been less attempts to use only monocular image sequences due to low accuracy. In addition, when depth prediction using only monocular images, only scale-inconsistent depth can be predicted, which is the reason why researchers are reluctant to use monocular images alone. Therefore, we propose a method for predicting absolute depth and detecting 3D objects using only monocular image sequences by enabling end-to-end learning of detection networks and depth prediction networks. As a result, the proposed method surpasses other existing methods in performance on the KITTI 3D dataset. Even when monocular image and 3D LiDAR are used together during training in an attempt to improve performance, ours exhibit is the best performance compared to other methods using the same input. In addition, end-to-end learning not only improves depth prediction performance, but also enables absolute depth prediction, because our network utilizes the fact that the size of a 3D object such as a car is determined by the approximate size.

The prevalence of hyperuricemia and chronic kidney disease (CKD) has been steadily increasing. The role of hyperuricemia and efficacy of uric acid-lowering agents against CKD progression remain controversial. This study aimed to evaluate the effect of hyperuricemia and uric acid-lowering agents on the progression of CKD. A total 2042 patients with CKD were analyzed in the KoreaN cohort Study for Outcomes in patients With Chronic Kidney Disease (KNOW-CKD), a prospective cohort study. Patients were classified into quartiles on the basis of their serum uric acid level and the prevalence of advanced CKD was higher in patients with a high uric acid level. A composite renal outcome was defined as one or more of the following: initiation of dialysis or transplantation, a two-fold increase in baseline serum creatinine levels, or a 50% decline in the estimated glomerular filtration rate during the follow-up period. A Cox proportional hazard ratio model was applied to analyze the relationship between composite renal outcome and uric acid levels. The risk of progression to renal failure increased by 28% (hazard ratio [HR], 1.277; 95% confidence interval [CI], 1.212–1.345) for each 1 mg/dl increase in the baseline uric acid level. In multivariate models, an association was found between the highest quartile of uric acid and increased risk of composite renal outcome (HR, 3.590; 95% CI, 2.546–5.063). A propensity score matching analysis was performed to survey the effect of uric acid lowering agent. Both allopurinol and febuxostat did not affect the renal outcome. In conclusion, hyperuricemia appears to be an independent risk factor for composite renal outcome, but allopurinol and febuxostat did not show reno-protective effect.

Few studies have investigated the incidence of cardiovascular disease (CVD) in the Asian chronic kidney disease (CKD) population. This study assessed the incidence of CVD, death, and a composite outcome of CVD and death in a prospective Korean predialysis CKD cohort. From a total of 2179 patients, incidence rates were analyzed, and competing risk analyses were conducted according to CKD stage. Additionally, incidence was compared to the general population. During a median 4.1 years of follow-up, the incidence of CVD, all-cause death, and the composite outcome was 17.2, 9.6, and 24.5 per 1000 person-years, respectively. These values were higher in diabetic vs. non-diabetic subjects ( P  < 0.001). For all outcomes, incidence rates increased with increasing CKD stage (CVD, P  = 0.001; death, P  < 0.001; and composite, P  < 0.001). Additionally, CKD stage G4 [hazard ratio (HR) 2.8, P  = 0.008] and G5 (HR 5.0, P  < 0.001) were significant risk factors for the composite outcome compared to stage G1 after adjustment. Compared to the general population, the total cohort population (stages G1–G5) showed significantly higher risk of CVD (HR 2.4, P  < 0.001) and the composite outcome (HR 1.7, P  < 0.001). The results clearly demonstrate that CKD is a risk factor for CVD in an Asian population.

The causes of chronic kidney disease (CKD) affects its outcomes. However, the relative risks for adverse outcomes according to specific causes of CKD is not well established. In a prospective cohort study from KNOW-CKD, a cohort was analyzed using overlap propensity score weighting methods. Patients were grouped into four categories according to the cause of CKD: glomerulonephritis (GN), diabetic nephropathy (DN), hypertensive nephropathy (HTN), or polycystic kidney disease (PKD). From a total of 2070 patients, the hazard ratio of kidney failure, the composite of cardiovascular disease (CVD) and mortality, and the slope of the estimated glomerular filtration rate (eGFR) decline according to the cause of CKD were compared between causative groups in a pairwise manner. There were 565 cases of kidney failure and 259 cases of composite CVD and death over 6.0 years of follow-up. Patients with PKD had a significantly increased risk for kidney failure compared to those with GN [Hazard ratio (HR) 1.82], HTN (HR 2.23), and DN (HR 1.73). For the composite outcome of CVD and death, the DN group had increased risks compared to the GN (HR 2.07), and HTN (HR 1.73) groups but not to the PKD group. The adjusted annual eGFR change for the DN and PKD groups were − 3.07 and − 3.37 mL/min/1.73 m 2 per year, respectively, and all of these values were significantly different than those of the GN and HTN groups (− 2.16 and − 1.42 mL/min/1.73 m 2 per year, respectively). In summary, the risk of kidney disease progression was relatively higher in patients with PKD compared to other causes of CKD. However, the composite of CVD and death was relatively higher in patients with DN-related CKD than in those with GN- and HTN-related CKD.