Type 2 diabetes is reported to be associated with olfactory dysfunction and cognitive decline. However, whether and how olfactory neural circuit abnormalities involve cognitive impairment in diabetes remains uncovered. This study thus aimed to investigate olfactory network alterations and the associations of odor-induced brain activity with cognitive and metabolic parameters in type 2 diabetes. Participants with normal cognition, including 51 patients with type 2 diabetes and 41 control subjects without diabetes, underwent detailed cognitive assessment, olfactory behavior tests, and odor-induced functional MRI measurements. Olfactory brain regions showing significantly different activation between the two groups were selected for functional connectivity analysis. Compared with the control subjects, patients with diabetes demonstrated significantly lower olfactory threshold score, decreased brain activation, and disrupted functional connectivity in the olfactory network. Positive associations of the disrupted functional connectivity with decreased neuropsychology test scores and reduced pancreatic function were observed in patients with diabetes. Notably, the association between pancreatic function and executive function was mediated by olfactory behavior and olfactory functional connectivity. Our results suggested the alteration of olfactory network is present before clinically measurable cognitive decrements in type 2 diabetes, bridging the gap between the central olfactory system and cognitive decline in diabetes.

Even though lifelong premature ejaculation (PE) is highly prevalent, few studies have investigated the neural mechanisms underlying PE. The extent and pattern of brain activation can be determined through a version of functional magnetic resonance imaging (fMRI) with erotic picture stimuli (task fMRI) and a resting-state fMRI (rs fMRI). We showed that the brain activity in the left inferior frontal gyrus and left insula was decreased both during the task and in the resting state, while there was higher activation in the right middle temporal gyrus during the task. Higher functional connectivity was found in PE between those three brain areas and the bilateral middle cingulate cortex, right middle frontal gyrus and supplementary motor area. Moreover, the brain activity had positive correlation with clinical rating scales, such as intravaginal ejaculatory latency time (IELT) and the Chinese Index of Premature Ejaculation (CIPE). These findings revealed that brain responses and functional integration in certain brain areas are impaired in cases of PE, which was consistently supported by multiple measurements obtained using a task and rs fMRI approach.

This study was aimed to investigate brain function connectivity in premature ejaculation (PE) patients using the functional connectivity density (FCD) and network property of resting-state functional magnetic resonance imaging. Twenty PE patients (mean age: 27.95 ± 4.52 years) and 15 normal controls (mean age: 27.87 ± 3.78 years) with no self-reported history of neurologic or psychiatric disease were enrolled in this study. International Index of Erectile Function-5 and Chinese Index of Sexual Function for Premature Ejaculation-5 questionnaires and self-reported intravaginal ejaculatory latency time (IELT) were obtained from each participant for symptom assessment. Two-sample t-tests (intergroup comparison) were applied in the short-range FCD (SFCD) analysis, long-range FCD (LFCD) analysis, region of interest–based analysis, and network topological organization analysis. Pearson correlation analysis was performed to correlate IELT with FCD or the network property. The patients with PE showed significantly decreased SFCD in the bilateral middle temporal gyrus, left orbitofrontal cortex, nucleus accumbens, fusiform, caudate, and thalamus (p < 0.05, AlphaSim-corrected). Notably, all these aforementioned brain areas are located in the dopamine pathway. In contrast, increased LFCD was observed in the left insula, Heschl's gyrus, putamen, bilateral precuneus, supplementary motor area, middle cingulate cortex, and anterior cingulate cortex in PE patients (p < 0.05, AlphaSim-corrected). In addition, the network topological analysis found reinforced network connectivity between several nodes. The degree of hub nodes increased in the patients with PE. IELT was positively correlated with SFCD and negatively correlated with LFCD or the degree of hub nodes (p < 0.05, Pearson correlation). In summary, our results are important for understanding the brain network in PE patients. The present findings indicate that PE patients have a significant synergism disorder across the region of dopamine pathway, which implied neuronal pathological changes might be related with the change of dopamine. The FCD and network property can serve as new disease severity biomarkers and therapeutic targets in PE.

Hyperuricemia and nonalcoholic fatty liver disease are global public health problems, which are strongly associated with metabolic syndrome. In this study, we demonstrate that uric acid induces hepatic fat accumulation via the ROS/JNK/AP-1 pathway. This study identifies a new mechanism of NAFLD pathogenesis and new potential therapeutic strategies for HUA-induced NAFLD.

Detecting small objects is a challenging task. We focus on a special case: the detection and classification of traffic signals in street views. We present a novel framework that utilizes a visual attention model to make detection more efficient, without loss of accuracy, and which generalizes. The attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and classified. In order to evaluate our method in the context of traffic signal detection, we have built a traffic light benchmark with over 15,000 traffic light instances, based on Tencent street view panoramas. We have tested our method both on the dataset we have built and the Tsinghua-Tencent 100K (TT100K) traffic sign benchmark. Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets. It is competitive with state-of-theart specialist traffic sign detectors on TT100K, but is an order of magnitude faster. To show generality, we tested it on the LISA dataset without tuning, and obtained an average precision in excess of 90%.