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Birds have a wide range of activities in the natural environment, and there are a lot of uncontrollable factors. Therefore, recording the neural signals and behavior data of birds in natural state transition is a major difficulty in the progress of neuroscience. At present, most neuroscience fields usually use wired EEG acquisition equipment, but due to its large size and heavy weight, it is not suitable for small animals, especially birds, to record neural and behavior data in natural state. In order to solve the above problems, we designed a wearable neuro-signal recording system in this study, which can synchronously record the neuro-signal and attitude data of birds in flight. The core processor of this system uses STM32 series high performance single-chip computer, and its peripheral components are described as follows: (1) 8-channel neural signal acquisition module based on AD1299; (2) GPS global positioning module; (3) Attitude measurement unit; (4) TF card storage module. The total weight of the system is about 20 g and the volume is about 27 mm x 25 mm x 15 mm. Finally, the neural signals and attitude data of the pigeons moving freely on the ground and in the sky were collected using the system we designed. The results showed that the system can not only stably record neural signals, attitude information and position information at the same time, but also does not affect their normal flight. The system has a good application prospect. Researchers can use it to record and analyze the neural signals of experimental animals, especially birds in free movement and even flight. Our work provides a portable and feasible experimental tool for further study of the neural mechanism of the brain in more complex environment.

Background: To compare the epidemiological characteristics of Sichuan Province, other provinces in China and the world epidemic trends by analyzing the prevalence and length of epidemic time. in order to provide a basis for responding to imported cases abroad and to formulate prevention and control strategies in areas that are still rapidly circulating.Methods: The number of confirmed cases, daily growth, incidence and the length of time from the first reported case to the end of local case(non-overseas imported cases) were compared by spatial and temporal (geographical, temporal) classification. Visualizing the development and changes of epidemic situation by layer through maps.Results: In the first wave, total of 539 cases were reported in Sichuan Province, with the incidence rate of 0.6462 / 100,000. The closer to Hubei, the heavier the epidemic. The peak of Sichuan Province came earlier and the value was lower. Eight weeks after Wuhan lockdown, all became better. The longest epidemic length in city level of China was 53 days, median 23 days. It was released quickly in the 1st month, and accelerated in the 2ed month (three times of 1st month). Most countries outside China began to rise rapidly 4 weeks after their first case. Some European countries was earlier than USA. Germany, Spain, Italy, and China cost 28, 29, 34, and 18 days to reached the peak of daily increment, after their daily increase up to 20 cases. Countries in African Region and South-East Asia Region were at the early stage, in Eastern Mediterranean Region and Region of the Americas were at rapid growth phase,in European presented an inflection point or at a plateau period but falling slowly.Conclusions: Adopting appropriate isolation and control measures is necessary to actively respond to the epidemic situation. If effective measures were implemented at the 8 key weeks, the peak value of the confirmed cases will be lower and decrease quickly. Some countries with improved epidemic situations also need to develop a continuous "local strategy at entry checkpoints" to respond to a possible second local epidemic.

Additional file 1. The molecular typing database of patients’ MRSA in Yunnnan province of China.

By the end of 2017, a total of 110,872 people living with HIV (PLWH) had been diagnosed in Sichuan, including 42,496 AIDS patients, making Sichuan one of the provinces heavily affected by the HIV/AIDS epidemic. The male/female ratio was 2.8:1; the 20–49 age group accounted for 68.5% of infections; and the Han ethnic majority accounted for 68.1% and the Yi ethnic minority accounted for 30.3% of the total cases. PLWH have a wide range of occupations, with farmers, unemployed, and rural migrant workers taking up the majority. Transmission by injection drug use accounted for the majority of cases until 2010, with sexual transmission rapidly expanding in the last decade. By the end of 2017, nearly 79,000 PLWH were receiving antiretroviral treatment in Sichuan province. Mortality greatly declined in recent years among those who received treatment. The number of HIV-related deaths surpassed 5000 in 2017. A comprehensive surveillance network has been set up composed of routine screening, case reporting, sentinel surveillance, specific epidemiological investigation, estimation of the HIV epidemic, and analysis of the incidence rate in different risk groups. Education activities targeting different groups have been carried out from provincial level to prefecture level and communities. Free antiretroviral treatment during the past decade has reduced the mortality rate among PLWH and has improved the quality of their life. Tailored behavioral interventions were implemented to effectively change high-risk behaviors among key populations. Prevention of mother-to-child transmission (PMTCT) has covered all 21 prefectures and 181 counties. A comprehensive prevention and treatment system that takes the community as its basis, health departments its mainstay, and professional institutes its backbone, with the coordination of security, civil administration and education, and the general public have been established. Remarkable progress has been made, but a great challenge lies ahead to control HIV sexual transmission, particularly in minorities.

Simple Summary A distributed brain network supports the goal-directed spatial learning of animals, in which the formation of the route from the current location to the goal is one of the central problems. To enhance our understanding of how the avian hippocampus (Hp) and nidopallium caudolaterale (NCL) cooperate during route formation, we examined neural activity in the Hp-NCL network of pigeons and explored their connectivity dynamics in a goal-directed spatial task. We found that pigeons’ behavioral changes during route formation are accompanied by modifications of their neural patterns in the Hp-NCL network. The depressed spectral power in Hp and NCL, together with the different dynamics of the functional connectivity in both regions, as well as, most importantly, the enhanced Hp-NCL theta functional connectivity, provide insight into the potential mechanism of avian spatial learning. Abstract Goal-directed spatial learning is crucial for the survival of animals, in which the formation of the route from the current location to the goal is one of the central problems. A distributed brain network comprising the hippocampus and prefrontal cortex has been shown to support such capacity, yet it is not fully understood how the most similar brain regions in birds, the hippocampus (Hp) and nidopallium caudolaterale (NCL), cooperate during route formation in goal-directed spatial learning. Hence, we examined neural activity in the Hp-NCL network of pigeons and explored the connectivity dynamics during route formation in a goal-directed spatial task. We found that behavioral changes in spatial learning during route formation are accompanied by modifications in neural patterns in the Hp-NCL network. Specifically, as pigeons learned to solve the task, the spectral power in both regions gradually decreased. Meanwhile, elevated hippocampal theta (5 to 12 Hz) connectivity and depressed connectivity in NCL were also observed. Lastly, the interregional functional connectivity was found to increase with learning, specifically in the theta frequency band during route formation. These results provide insight into the dynamics of the Hp-NCL network during spatial learning, serving to reveal the potential mechanism of avian spatial navigation.

BACKGROUND: Catheter-based renal denervation (RDN) has emerged as a promising treatment option for hypertension. However, randomized controlled trials (RCTs) have reported conflicting results on blood pressure (BP) reduction. Patient- and procedure-related confounders have been implied as the potential sources of inconsistent BP responses. We aimed to investigated whether unplanned and frequent medication changes in RDN studies affected the BP response to RDN by conducting sensitivity and subgroup analyses, according to antihypertensive medication change rates in a meta-analysis of RCTs. METHODS: We searched the PUBMED, EMBASE, and COCHRANE databases up to May 2018. RCTs that studied the effects of RDN on hypertensive patients were included. A meta-analysis was carried out using RevMan 5.3. RESULTS: A total of 12 studies were included, of which four fulfilled the inclusion criteria of < 10% medication change rate in our review. Subgroup meta-analyses of the four RCTs with < 10% medication change rates showed statistically significant reductions of 6.07 mmHg and 7.12 mmHg in 24-hour and office systolic BP, respectively. The 24-hour and office diastolic BP were also reduced (mean difference = -3.89 mmHg and -4.27 mmHg, respectively). These subgroup analyses had no heterogeneity (I(2) = 0%). In contrast, the pooled analysis of the 12 studies and the subgroup analysis of eight studies with > 10% medication change rates both had a high level of heterogeneity and no significant BP reduction. CONCLUSIONS: The effectiveness of RDN was demonstrated across a broad range of antihypertensive medications used at baseline after removing the confounder of unplanned medication changes.

Antiretroviral therapy (ART) has been introduced recently and has significantly impacted morbidity and mortality, but can also engender drug resistance. To identify the prevalence of HIV-1 drug resistance (HIVDR) among patients with antiretroviral therapy failure in Sichuan during the period from 2010 to 2016, we carried out a longitudinal study in Sichuan, a province with the highest HIV/AIDS prevalence in China. The data and blood samples were collected from HIV/AIDS patients who received ART for more than half a year. Overall 5,512 sequences were completed from 7,059 ART-failure patients, and 2,499 individuals were identified as drug resistant. Among those with HIVDR mutations identified, 25.37% were against non-nucleoside reverse transcriptase inhibitors (NNRTIs), and 1.60% was against nucleoside reverse transcriptase inhibitors (NRTIs). NRTI-resistant drugs were mainly lamivudine (3TC) (57.77%) and emtricitabine (FTC), while NNRTI-resistant drugs were mainly nevirapine (NVP) (91.13%) and efavirenz (EFV) (72.81%). The most common recombination subtypes of HIV-1 in sequenced samples were CRF07_BC (circulating recombinant form, CRF) (41.42%), followed by CRF01_AE (40.77%). Moreover, drug resistance rate increased with the prolongation of treatment time (χ2 = 14.758, P < 0.05). The overall prevalence of acquired drug resistance in HIV-1 infected patients in Sichuan was 5.47%, which has remained relatively stable from 2010 to 2016. HIV-1 CRF01_AE and CRF07_BC subtypes were the main epidemic strains, and the possibility of resistance was higher in CRF01_AE subtypes. The current study highlights the importance of acquired drug resistance surveillance over a long period.

Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization. As the adverse effects of clickbait attract more and more attention, researchers have started to explore machine learning techniques to automatically detect clickbaits. Previous work on clickbait detection assumes that all the training data is available locally during training. In many real-world applications, however, training data is generally distributedly stored by different parties (e.g., different parties maintain data with different feature spaces), and the parties cannot share their data with each other due to data privacy issues. It is challenging to build models of high-quality federally for detecting clickbaits effectively without data sharing. In this paper, we propose a federated training framework, which is called federated hierarchical hybrid networks, to build clickbait detection models, where the titles and contents are stored by different parties, whose relationships must be exploited for clickbait detection. We empirically demonstrate that our approach is effective by comparing our approach to the state-of-the-art approaches using datasets from social media., Comment: 10 pages

Figure S1. DAST NWs preparation. (DOC 2375 kb)