Intensity-dependent effects of tDCS on motor learning are related to dopamine.

Non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), are popular methods for inducing neuroplastic changes to alter cognition and behaviour. One challenge for the field is to optimise stimulation protocols to maximise benefits. For this to happen, we need a better understanding of how stimulation modulates cortical functioning/behaviour. To date, there is increasing evidence for a dose-response relationship between tDCS and brain excitability, however how this relates to behaviour is not well understood. Even less is known about the neurochemical mechanisms which may drive the dose-response relationship between stimulation intensities and behaviour. Here, we examine the effect of three different tDCS stimulation intensities (1 mA, 2 mA, 4 mA anodal motor cortex tDCS) administered during the explicit learning of motor sequences. Further, to assess the role of dopamine in the dose-response relationship between tDCS intensities and behaviour, we examined how pharmacologically increasing dopamine availability, via 100 mg of levodopa, modulated the effect of stimulation on learning. In the absence of levodopa, we found that 4 mA tDCS improved and 1 mA tDCS impaired acquisition of motor sequences relative to sham stimulation. Conversely, levodopa reversed the beneficial effect of 4 mA tDCS. This effect of levodopa was no longer evident at the 48-h follow-up, consistent with previous work characterising the persistence of neuroplastic changes in the motor cortex resulting from combining levodopa with tDCS. These results provide the first direct evidence for a role of dopamine in the intensity-dependent effects of tDCS on behaviour.

Liraglutide improved the reproductive function of obese mice by upregulating the testicular AC3/cAMP/PKA pathway.

BACKGROUND: The incidence of male reproductive dysfunction is increasing annually, and many studies have shown that obesity can cause severe harm to male reproductive function. The mechanism of male reproductive dysfunction caused by obesity is unclear, and there is no ideal treatment. Identification of effective therapeutic drugs and elucidation of the molecular mechanism involved in male reproductive health are meaningful. In this study, we investigated the effects of the GLP-1 receptor agonist liraglutide on sex hormones, semen quality, and testicular AC3/cAMP/PKA levels in high-fat-diet-induced obese mice. METHODS: Obese mice and their lean littermates were treated with liraglutide or saline for 12 weeks. Body weight was measured weekly. Fasting blood glucose (FBG) was measured using a blood glucose test strip. The serum levels of insulin (INS), luteinizing hormone (LH), follicle stimulating hormone (FSH), testosterone (T), free testosterone (F-TESTO), estradiol (E2), and sex hormone binding globulin (SHBG) were detected using ELISA. The sperm morphology and sperm count were observed after Pap staining. The mRNA and protein expression levels of testicular GLP-1R and AC3 were measured by RT-qPCR and Western blot, respectively. Testicular cAMP levels and PKA activity were detected using ELISA. RESULTS: Liraglutide treatment can decrease body weight, FBG, INS, HOMA-IR, E2 and SHBG levels; increase LH, FSH, T, and F-TESTO levels; increase sperm count; decrease the sperm abnormality rate; and increase GLP-1R and AC3 expression levels and cAMP levels and PKA activity in testicular tissue. CONCLUSIONS: Liraglutide can improve the sex hormone levels and semen quality of obese male mice. In addition to its weight loss effect, liraglutide can improve the reproductive function of obese male mice, which may also be related to the upregulation of AC3/cAMP/PKA pathway in the testis. This work lays the groundwork for future clinical studies.

Attentional capture by fearful faces requires consciousness and is modulated by task-relevancy: A dot-probe EEG study.

In the current EEG study, we used a dot-probe task in conjunction with backward masking to examine the neural activity underlying awareness and spatial processing of fearful faces and the neural processes for subsequent cued spatial targets. We presented face images under different viewing conditions (subliminal and supraliminal) and manipulated the relation between a fearful face in the pair and a subsequent target. Our mass univariate analysis showed that fearful faces elicit the N2-posterior-contralateral, indexing spatial attention capture, only when they are presented supraliminally. Consistent with this, the multivariate pattern analysis revealed a successful decoding of the location of the fearful face only in the supraliminal viewing condition. Additionally, the spatial attention capture by fearful faces modulated the processing of subsequent lateralised targets that were spatially congruent with the fearful face, in both al and electrophysiological data. There was no evidence for nonconscious processing of the fearful faces in the current paradigm. We conclude that spatial attentional capture by fearful faces requires visual awareness and it is modulated by top-down task demands.

A Novel Multi-Focus Image Fusion Network with U-Shape Structure.

Multi-focus image fusion has become a very practical image processing task. It uses multiple images focused on various depth planes to create an all-in-focus image. Although extensive studies have been produced, the performance of existing methods is still limited by the inaccurate detection of the focus regions for fusion. Therefore, in this paper, we proposed a novel U-shape network which can generate an accurate decision map for the multi-focus image fusion. The Siamese encoder of our U-shape network can preserve the low-level cues with rich spatial details and high-level semantic information from the source images separately. Moreover, we introduce the ResBlocks to expand the receptive field, which can enhance the ability of our network to distinguish between focus and defocus regions. Moreover, in the bridge stage between the encoder and decoder, the spatial pyramid pooling is adopted as a global perception fusion module to capture sufficient context information for the learning of the decision map. Finally, we use a hybrid loss that combines the binary cross-entropy loss and the structural similarity loss for supervision. Extensive experiments have demonstrated that the proposed method can achieve the state-of-the-art performance.