REV-ERB in GABAergic neurons controls diurnal hepatic insulin sensitivity.

Systemic insulin sensitivity shows a diurnal rhythm with a peak upon waking1,2. The molecular mechanism that underlies this temporal pattern is unclear. Here we show that the nuclear receptors REV-ERB-α and REV-ERB-ß (referred to here as 'REV-ERB') in the GABAergic (γ-aminobutyric acid-producing) neurons in the suprachiasmatic nucleus (SCN) (SCNGABA neurons) control the diurnal rhythm of insulin-mediated suppression of hepatic glucose production in mice, without affecting diurnal eating or locomotor behaviours during regular light-dark cycles. REV-ERB regulates the rhythmic expression of genes that are involved in neurotransmission in the SCN, and modulates the oscillatory firing activity of SCNGABA neurons. Chemogenetic stimulation of SCNGABA neurons at waking leads to glucose intolerance, whereas restoration of the temporal pattern of either SCNGABA neuron firing or REV-ERB expression rescues the time-dependent glucose metabolic phenotype caused by REV-ERB depletion. In individuals with diabetes, an increased level of blood glucose after waking is a defining feature of the 'extended dawn phenomenon'3,4. Patients with type 2 diabetes with the extended dawn phenomenon exhibit a differential temporal pattern of expression of REV-ERB genes compared to patients with type 2 diabetes who do not have the extended dawn phenomenon. These findings provide mechanistic insights into how the central circadian clock regulates the diurnal rhythm of hepatic insulin sensitivity, with implications for our understanding of the extended dawn phenomenon in type 2 diabetes.

Cerebrospinal Fluid Protein Markers Indicate Neuro-Damage in SARS-CoV-2-Infected Nonhuman Primates.

Neurologic manifestations are among the most frequently reported complications of COVID-19. However, given the paucity of tissue samples and the highly infectious nature of the etiologic agent of COVID-19, we have limited information to understand the neuropathogenesis of COVID-19. Therefore, to better understand the impact of COVID-19 on the brain, we used mass-spectrometry-based proteomics with a data-independent acquisition mode to investigate cerebrospinal fluid (CSF) proteins collected from two different nonhuman primates, Rhesus Macaque and African Green Monkeys, for the neurologic effects of the infection. These monkeys exhibited minimal to mild pulmonary pathology but moderate to severe central nervous system (CNS) pathology. Our results indicated that CSF proteome changes after infection resolution corresponded with bronchial virus abundance during early infection and revealed substantial differences between the infected nonhuman primates and their age-matched uninfected controls, suggesting these differences could reflect altered secretion of CNS factors in response to SARS-CoV-2-induced neuropathology. We also observed the infected animals exhibited highly scattered data distributions compared to their corresponding controls indicating the heterogeneity of the CSF proteome change and the host response to the viral infection. Dysregulated CSF proteins were preferentially enriched in functional pathways associated with progressive neurodegenerative disorders, hemostasis, and innate immune responses that could influence neuroinflammatory responses following COVID-19. Mapping these dysregulated proteins to the Human Brain Protein Atlas found that they tended to be enriched in brain regions that exhibit more frequent injury following COVID-19. It, therefore, appears reasonable to speculate that such CSF protein changes could serve as signatures for neurologic injury, identify important regulatory pathways in this process, and potentially reveal therapeutic targets to prevent or attenuate the development of neurologic injuries following COVID-19.

The global magnitude and temporal trend of rheumatoid arthritis burden attributable to smoking from 1990 to 2019.

OBJECTIVES: The relationship between smoking and RA has been confirmed. Most nations have ratified the Framework Convention on Tobacco Control. However, there are considerable regional differences in how effectively tobacco control measures were implemented. This study was carried out to estimate the spatiotemporal trends of smoking-related RA burdens. METHODS: Data were available from the Global Burden of Disease Study 2019 and were analysed by age, sex, year and region. Joinpoint regression analysis was applied to the analysis of temporal trends in the RA burden resulting from smoking over 30 years. RESULTS: From 1990 to 2019, the number of global RA cases increased each year. The age-standardized prevalence, death and disability-adjusted life-year (DALY) rates also increased. However, there was a wave in the changing trend of the age-standardized death rate, with the lowest point in 2012 and the highest point in 1990. Smoking, in particular, was responsible for 11.9% of total RA deaths and 12.8% of total DALYs in 1990 but only 8.5% of total RA deaths and 9.6% of total DALYs in 2019. A greater burden from smoking exposure was borne by men, older adults and people living in high-middle and high sociodemographic index (SDI) countries and regions. Moreover, the UK demonstrated the highest reduction in age-standardized death and DALY rates over the three decades. CONCLUSION: There were reductions in the age-standardized burdens of RA caused by smoking worldwide. Nevertheless, this continues to be an issue in some areas, and efforts to reduce smoking should be made to lessen this growing burden.

Design, synthesis and mechanism research of novel algicide based on bioactive fragments synthesis strategy.

The frequency and intensity of harmful cyanobacterial blooms (HCBs) are increasing all over the world, their prevention and control have become a great challenge. In this paper, a series of 1,3,4-thiadiazole thioacetamides (T series) were designed and synthesized as potential algaecides. Among them, the compound T3 showed its best algacidal activity against Synechocystis sp. PCC 6803 (PCC 6803, EC50 = 1.51 µM) and Microcystis aeruginosa FACHB 905 (FACHB905, EC50 = 4.88 µM), which was more effective than the lead compound L1 (PCC6803, EC50 = 7.7 µM; FACHB905, EC50 = 8.8 µM) and the commercially available herbicide prometryn (PCC6803, EC50 = 4.64 µM；FACHB905, EC50 = 6.52 µM). Meanwhile, T3 showed a lower inhibitory activity (EC50 = 12.76 µM) than prometryn (EC50 = 7.98 µM) to Chlorella FACHB1227, indicating that T3 had selective inhibition to prokaryotic algae (PCC6803, FACHB905) and eukaryotic algae (FACHB1227). Furthermore, the algacidal and anti-algae activities of T3 were significantly better than those of prometryn, while the toxicity of zebrafish and human cells was less than prometryn. Electron microscope, physiological, biochemical and metabonomic analysis showed that T3 interfered with light absorption and light conversion during photosynthesis by significantly reducing chlorophyll content, thus inhibited metabolic pathways such as the Calvin cycle and TCA cycle, and eventually led to the cell rupture of cyanobacteria. These results afforded further development of effective and safe algaecides.

A Novel Catheter Shape-Sensing Method Based on Deep Learning with a Multi-Core Optical Fiber.

In this paper, we propose a novel shape-sensing method based on deep learning with a multi-core optical fiber for the accurate shape-sensing of catheters and guidewires. Firstly, we designed a catheter with embedded multi-core fiber containing three sensing outer cores and one temperature compensation middle core. Then, we analyzed the relationship between the central wavelength shift, the curvature of the multi-core Fiber Bragg Grating (FBG), and temperature compensation methods to establish a Particle Swarm Optimization (PSO) BP neural network-based catheter shape sensing method. Finally, experiments were conducted in both constant and variable temperature environments to validate the method. The average and maximum distance errors of the PSO-BP neural network were 0.57 and 1.33 mm, respectively, under constant temperature conditions, and 0.36 and 0.96 mm, respectively, under variable temperature conditions. This well-sensed catheter shape demonstrates the effectiveness of the shape-sensing method proposed in this paper and its potential applications in real surgical catheters and guidewire.

Barbadin Potentiates Long-Term Effects of Lorcaserin on POMC Neurons and Weight Loss.

Obesity is a serious global health problem because of its increasing prevalence and comorbidities, but its treatments are limited. The serotonin 2C receptor (5-HT2CR), a G-protein-coupled receptor, activates proopiomelanocortin (POMC) neurons in the arcuate nucleus of hypothalamus (ARH) to reduce appetite and weight gain. However, several 5-HT analogs targeting this receptor, e.g., lorcaserin (Lor), suffer from diminished efficacy to reduce weight after prolonged administration. Here, we show that barbadin (Bar), a novel ß-arrestin/ß2-adaptin inhibitor, can prevent 5-HT2CR internalization in cells and potentiate long-term effects of Lor to reduce appetite and body weight in male mice. Mechanistically, we demonstrate that Bar co-treatment can effectively maintain the sensitivity of the 5-HT2CR in POMCARH neurons, despite prolonged Lor exposure, thereby allowing these neurons to be activated through opening the transient receptor potential cation (TRPC) channels. Thus, our results prove the concept that inhibition of 5-HT2CR desensitization can be a valid strategy to improve the long-term weight loss effects of Lor or other 5-HT2CR agonists, and also provide an intellectual framework to develop effective long-term management of weight by targeting 5-HT2CR desensitization.SIGNIFICANCE STATEMENT By demonstrating that the combination of barbadin (Bar) with a G-protein-coupled receptor (GPCR) agonist can provide prolonged weight-lowering benefits in a preclinical setting, our work should call for additional efforts to validate Bar as a safe and effective medicine or to use Bar as a lead compound to develop more suitable compounds for obesity treatment. These results prove the concept that inhibition of serotonin 2C receptor (5-HT2CR) desensitization can be a valid strategy to improve the long-term weight loss effects of lorcaserin (Lor) or other 5-HT2CR agonists. Since GPCRs represent a major category as therapeutic targets for various human diseases and desensitization of GPCRs is a common issue, our work may provide a conceptual framework to enhance effects of a broad range of GPCR medicines.

Reciprocal control of obesity and anxiety-depressive disorder via a GABA and serotonin neural circuit.

The high comorbidity between obesity and mental disorders, such as depression and anxiety, often exacerbates metabolic and neurological symptoms significantly. However, neural mechanisms that underlie reciprocal control of feeding and mental states are largely elusive. Here we report that melanocortin 4 receptor (MC4R) neurons located in the dorsal bed nucleus of the stria terminus (dBNST) engage in the regulation of mentally associated weight gain by receiving GABAergic projections from hypothalamic AgRP neurons onto α5-containing GABAA receptors and serotonergic afferents onto 5-HT3 receptors. Chronic treatment with a high-fat diet (HFD) significantly blunts the hyperexcitability of AgRP neurons in response to not only hunger but also anxiety and depression-like stimuli. Such HFD-mediated desensitization reduces GABAergic outputs from AgRP neurons to downstream MC4RdBNST neurons, resulting in severe mental dysregulation. Genetic enhancement of the GABAAR-α5 or suppression of the 5-HT3R within the MC4RdBNST neurons not only abolishes HFD-induced anxiety and depression but also robustly reduces body weight by suppression of food intake. To gain further translational insights, we revealed that combined treatment of zonisamide (enhancing the GABAAR-α5 signaling) and granisetron (a selective 5-HT3R antagonist) alleviates mental dysfunction and yields a robust reversal of diet-induced obesity by reducing total calorie intake and altering food preference towards a healthy low-fat diet. Our results unveil a neural mechanism for reciprocal control of appetite and mental states, which culminates in a novel zonisamide-granisetron cocktail therapy for potential tackling the psychosis-obesity comorbidity.

5-HT recruits distinct neurocircuits to inhibit hunger-driven and non-hunger-driven feeding.

Obesity is primarily a consequence of consuming calories beyond energetic requirements, but underpinning drivers have not been fully defined. 5-Hydroxytryptamine (5-HT) neurons in the dorsal Raphe nucleus (5-HTDRN) regulate different types of feeding behavior, such as eating to cope with hunger or for pleasure. Here, we observed that activation of 5-HTDRN to hypothalamic arcuate nucleus (5-HTDRN → ARH) projections inhibits food intake driven by hunger via actions at ARH 5-HT2C and 5-HT1B receptors, whereas activation of 5-HTDRN to ventral tegmental area (5-HTDRN → VTA) projections inhibits non-hunger-driven feeding via actions at 5-HT2C receptors. Further, hunger-driven feeding gradually activates ARH-projecting 5-HTDRN neurons via inhibiting their responsiveness to inhibitory GABAergic inputs; non-hunger-driven feeding activates VTA-projecting 5-HTDRN neurons through reducing a potassium outward current. Thus, our results support a model whereby parallel circuits modulate feeding behavior either in response to hunger or to hunger-independent cues.

A POMC-originated circuit regulates stress-induced hypophagia, depression, and anhedonia.

Chronic stress causes dysregulations of mood and energy homeostasis, but the neurocircuitry underlying these alterations remain to be fully elucidated. Here we demonstrate that chronic restraint stress in mice results in hyperactivity of pro-opiomelanocortin neurons in the arcuate nucleus of the hypothalamus (POMCARH neurons) associated with decreased neural activities of dopamine neurons in the ventral tegmental area (DAVTA neurons). We further revealed that POMCARH neurons project to the VTA and provide an inhibitory tone to DAVTA neurons via both direct and indirect neurotransmissions. Finally, we show that photoinhibition of the POMCARH→VTA circuit in mice increases body weight and food intake, and reduces depression-like behaviors and anhedonia in mice exposed to chronic restraint stress. Thus, our results identified a novel neurocircuitry regulating feeding and mood in response to stress.

Catalyst-Controlled Divergent Intramolecular Cyclizations of Morita-Baylis-Hillman Carbonates.

Unprecedented catalyst-controlled divergent cyclizations of Morita-Baylis-Hillman carbonates have been established using either Cs2CO3 or quinuclidine as the catalyst. 1,2-Dihydroquinolines and 4H-3,1-benzoxazines were efficiently synthesized via SN2' and SN2'-SN2' processes, respectively. DFT calculations have led to a deeper understanding of these reaction modes and chemoselectivity.

The bonding variation of γ-TiAl during deformation.

To improve the ductility of γ-TiAl, the complicated interaction of Ti-Al chemical bonds, Al-Al covalent bonds, and Ti metallic bonds in the process of deformation should be evaluated. The electronic structure variation of γ-TiAl during deformation is investigated using first-principles calculation with the local energy schemes. The relative influence of these bonds on atomic stability is for the first time qualitatively evaluated by the combination of local energy schemes and Electron Localization Function (ELF) analysis. The discrepant influence pattern of some alloy elements on ductility is discussed based on the bonding analysis.

Shape reconstruction based on a multicore optical fiber array with temperature self-compensation.

Temperature variations affect the accuracy of fiber-optic shape sensors; thus, temperature compensation is particularly important. This study developed a temperature self-compensation algorithm and verified the measuring accuracy of shape sensors after temperature compensation. A multicore fiber Bragg grating (FBG) sensor array was calibrated to confirm the consistency of sensor characteristics, and the relationship between the curvature and wavelength shift of FBGs was studied. A variable-temperature experiment revealed the temperature sensitivity of the FBG sensors, and these results were used by the temperature self-compensation algorithm. Further, shape reconstruction before and after temperature compensation was studied. The deformed shapes of the multicore FBG sensor array under different bending conditions were reconstructed. The results obtained after temperature compensation show that the average error between the measured and the theoretical coordinate values as less than 0.33 mm, the maximum error as less than 5.61 mm, and the relative error as less than 3.50%. The proposed temperature self-compensation algorithm has excellent prospects for application to flexible structures.

Novel Targets in Glucose Homeostasis and Obesity-Lesson from Rare Mutations.

PURPOSE OF REVIEW: Obesity and diabetes have already become the second largest risk factor for cardiovascular disease. During the last decade, remarkable advances have been made in understanding the human genome's contribution to glucose homeostasis disorders and obesity. A few studies on rare mutations of candidate genes provide potential genetic targets for the treatment of diabetes and obesity. In this review, we discussed the detailed findings of these studies and the possible causalities between specific genetic variations and dysfunctions in energy or glucose homeostasis. We are optimistic that novel therapeutic strategies targeting these specific mutants for treating and preventing diabetes and obesity will be developed in the near future. RECENT FINDINGS: Studies on rare genetic mutation-caused obesity or diabetes have identified potential genetic targets to decrease body weight or reduce the risk of diabetes. Rare mutations observed in lipodystrophy, obese, or diabetic human patients are promising targets in the treatment of diabetes and obesity.

Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis.

Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance. The proposed model is composed of three functional modules. The first module mainly performs the function of removing redundant genes. The main feature genes are selected using minimum redundancy maximum relevance (mRMR) method to reduce the dimensionality of features thereby increasing the prediction results. The second module aims to solve the problem caused by imbalanced data using hybrid sampling algorithm RUSBoost. The third module focuses on the classification algorithm optimization. We use mixed kernel function (MKF) based support vector machine (SVM) model to classify an unknown sample into healthy individuals and CRC patients, and then, the Whale Optimization Algorithm (WOA) is applied to find most optimal parameters of the proposed MKF-SVM. The final results show that the proposed model achieves higher G-means than other comparable models. The conclusion comes to show that RUSBoost wrapping WOA + MKF-SVM model can be applied to improve the predictive performance of colorectal cancer based on the imbalanced data.

Optical Fiber Sensor Performance Evaluation in Soft Polyimide Film with Different Thickness Ratios.

To meet the application requirements of curvature measurement for soft biomedical robotics and flexible morphing wings of aircraft, the optical fiber Bragg grating (FBG) shape sensor for soft robots and flexible morphing wing was implemented. This optical FBG is embedded in polyimide film and then fixed in the body of a soft robot and morphing wing. However, a lack of analysis on the embedded depth of FBG sensors in polyimide film and its sensitivity greatly limits their application potential. Herein, the relationship between the embedded depth of the FBG sensor in polyimide film and its sensitivity and stability are investigated. The sensing principle and structural design of the FBG sensor embedded in polyimide film are introduced; the bending curvatures of the FBG sensor and its wavelength shift in polyimide film are studied; and the relationship between the sensitivity, stability, and embedded depth of these sensors are verified experimentally. The results showed that wavelength shift and curvature have a linear relationship. With the sensor's curvature ranging from 0 m-1 to 30 m-1, their maximum sensitivity is 50.65 pm/m-1, and their minimum sensitivity is 1.96 pm/m-1. The designed FBG sensor embedded in polyimide films shows good consistency in repeated experiments for soft actuator and morphing wing measurement; the FBG sensing method therefore has potential for real applications in shape monitoring in the fields of soft robotics and the flexible morphing wings of aircraft.

Visual Detection and Tracking System for a Spherical Amphibious Robot.

With the goal of supporting close-range observation tasks of a spherical amphibious robot, such as ecological observations and intelligent surveillance, a moving target detection and tracking system was designed and implemented in this study. Given the restrictions presented by the amphibious environment and the small-sized spherical amphibious robot, an industrial camera and vision algorithms using adaptive appearance models were adopted to construct the proposed system. To handle the problem of light scattering and absorption in the underwater environment, the multi-scale retinex with color restoration algorithm was used for image enhancement. Given the environmental disturbances in practical amphibious scenarios, the Gaussian mixture model was used to detect moving targets entering the field of view of the robot. A fast compressive tracker with a Kalman prediction mechanism was used to track the specified target. Considering the limited load space and the unique mechanical structure of the robot, the proposed vision system was fabricated with a low power system-on-chip using an asymmetric and heterogeneous computing architecture. Experimental results confirmed the validity and high efficiency of the proposed system. The design presented in this paper is able to meet future demands of spherical amphibious robots in biological monitoring and multi-robot cooperation.

Apolipoprotein A-IV Inhibits AgRP/NPY Neurons and Activates Pro-Opiomelanocortin Neurons in the Arcuate Nucleus.

BACKGROUND/AIMS: Apolipoprotein A-IV (apoA-IV) in the brain potently suppresses food intake. However, the mechanisms underlying its anorexigenic effects remain to be identified. METHODS: We first examined the effects of apoA-IV on cellular activities in hypothalamic neurons that co-express agouti-related peptide (AgRP) and neuropeptide Y (NPY) and in neurons that express pro-opiomelanocortin (POMC). We then compared anorexigenic effects of apoA-IV in wild-type mice and in mutant mice lacking melanocortin 4 receptors (MC4Rs; the receptors of AgRP and the POMC gene product). Finally, we examined expression of apoA-IV in mouse hypothalamus and quantified its protein levels at fed versus fasted states. RESULTS: We demonstrate that apoA-IV inhibited the firing rate of AgRP/NPY neurons. The decreased firing was associated with hyperpolarized membrane potential and decreased miniature excitatory postsynaptic current. We further used c-fos immunoreactivity to show that intracerebroventricular (i.c.v.) injections of apoA-IV abolished the fasting-induced activation of AgRP/NPY neurons in mice. Further, we found that apoA-IV depolarized POMC neurons and increased their firing rate. In addition, genetic deletion of MC4Rs blocked anorexigenic effects of i.c.v. apoA-IV. Finally, we detected endogenous apoA-IV in multiple neural populations in the mouse hypothalamus, including AgRP/NPY neurons, and food deprivation suppressed hypothalamic apoA-IV protein levels. CONCLUSION: Our findings support a model where central apoA-IV inhibits AgRP/NPY neurons and activates POMC neurons to activate MC4Rs, which in turn suppresses food intake.

Entropy and distance-guided super self-ensembling for optic disc and cup segmentation.

Segmenting the optic disc (OD) and optic cup (OC) is crucial to accurately detect changes in glaucoma progression in the elderly. Recently, various convolutional neural networks have emerged to deal with OD and OC segmentation. Due to the domain shift problem, achieving high-accuracy segmentation of OD and OC from different domain datasets remains highly challenging. Unsupervised domain adaptation has taken extensive focus as a way to address this problem. In this work, we propose a novel unsupervised domain adaptation method, called entropy and distance-guided super self-ensembling (EDSS), to enhance the segmentation performance of OD and OC. EDSS is comprised of two self-ensembling models, and the Gaussian noise is added to the weights of the whole network. Firstly, we design a super self-ensembling (SSE) framework, which can combine two self-ensembling to learn more discriminative information about images. Secondly, we propose a novel exponential moving average with Gaussian noise (G-EMA) to enhance the robustness of the self-ensembling framework. Thirdly, we propose an effective multi-information fusion strategy (MFS) to guide and improve the domain adaptation process. We evaluate the proposed EDSS on two public fundus image datasets RIGA+ and REFUGE. Large amounts of experimental results demonstrate that the proposed EDSS outperforms state-of-the-art segmentation methods with unsupervised domain adaptation, e.g., the Dicemean score on three test sub-datasets of RIGA+ are 0.8442, 0.8772 and 0.9006, respectively, and the Dicemean score on the REFUGE dataset is 0.9154.

BGF-Net: Boundary guided filter network for medical image segmentation.

How to fuse low-level and high-level features effectively is crucial to improving the accuracy of medical image segmentation. Most CNN-based segmentation models on this topic usually adopt attention mechanisms to achieve the fusion of different level features, but they have not effectively utilized the guided information of high-level features, which is often highly beneficial to improve the performance of the segmentation model, to guide the extraction of low-level features. To address this problem, we design multiple guided modules and develop a boundary-guided filter network (BGF-Net) to obtain more accurate medical image segmentation. To the best of our knowledge, this is the first time that boundary guided information is introduced into the medical image segmentation task. Specifically, we first propose a simple yet effective channel boundary guided module to make the segmentation model pay more attention to the relevant channel weights. We further design a novel spatial boundary guided module to complement the channel boundary guided module and aware of the most important spatial positions. Finally, we propose a boundary guided filter to preserve the structural information from the previous feature map and guide the model to learn more important feature information. Moreover, we conduct extensive experiments on skin lesion, polyp, and gland segmentation datasets including ISIC 2016, CVC-EndoSceneStil and GlaS to test the proposed BGF-Net. The experimental results demonstrate that BGF-Net performs better than other state-of-the-art methods.