Adaptation of Two Wild Bird-Origin H3N8 Avian Influenza Viruses to Mammalian Hosts.

Wild birds play an important role in the emergence, evolution, and spread of zoonotic avian influenza viruses (AIVs). However, there are few studies on the cross-species transmission of the H3N8 AIV originating from wild birds. In this study, we investigated the transmissibility and pathogenicity of two H3N8 low pathogenic avian influenza viruses (LPAIVs) isolated from wild birds, GZA1 and XJ47, to mammals. The HA genes of both strains belonged to Eurasian isolates, while the other genes were derived from a variety of other subtypes of AIVs. Both strains can infect specific-pathogen-free (SPF) chickens, BALB/c mice, and guinea pigs. The XJ47 strain spread horizontally in SPF chickens and guinea pigs. The GZA1 strain did not spread horizontally but caused higher weight loss and mild lung inflammation in mice. P12-GZA1- and P12-XJ47-adapted strains obtained after 12 passages in the lung of mice showed enhanced pathogenicity in mice, which led to obvious clinical symptoms, lung inflammation, and 100% death. Both adapted strains have the reported mutation T97I in the PA, and the reported mutation D701N in PB2 has been found in the P12-GZA1-adapted strain. This study provides an important scientific basis for the continuous monitoring of wild AIVs and the mechanism underlying AIV cross-species transmission.

Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity.

At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.

Infectious Bronchitis Virus Infection Increases Pathogenicity of H9N2 Avian Influenza Virus by Inducing Severe Inflammatory Response.

Infectious bronchitis virus (IBV) and H9N2 avian influenza virus (AIV) are frequently identified in chickens with respiratory disease. However, the role and mechanism of IBV and H9N2 AIV co-infection remain largely unknown. Specific-pathogen-free (SPF) chickens were inoculated with IBV 2 days before H9N2 virus inoculation (IBV/H9N2); with IBV and H9N2 virus simultaneously (IBV+H9N2); with H9N2 virus 2 days before IBV inoculation (H9N2/IBV); or with either IBV or H9N2 virus alone. Severe respiratory signs, pathological damage, and higher morbidity and mortality were observed in the co-infection groups compared with the IBV and H9N2 groups. In general, a higher virus load and a more intense inflammatory response were observed in the three co-infection groups, especially in the IBV/H9N2 group. The same results were observed in the transcriptome analysis of the trachea of the SPF chickens. Therefore, IBV might play a major role in the development of respiratory disease in chickens, and secondary infection with H9N2 virus further enhances the pathogenicity by inducing a severe inflammatory response. These findings may provide a reference for the prevention and control of IBV and H9N2 AIV in the poultry industry and provide insight into the molecular mechanisms of IBV and H9N2 AIV co-infection in chickens.

Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia.

Previous studies have reported that schizophrenia (SZ) patients showed selective reinforcement learning deficits and abnormal feedback-related event-related potential (ERP) components. However, how the brain networks and their topological properties evolve over time during transient feedback-related cognition processing in SZ patients has not been investigated so far. In this paper, using publicly available feedback-related ERP data which were recorded from SZ patients and healthy controls (HC) when they performed a reinforcement learning task, we carried out an event-related network analysis where topology of brain functional networks was characterized with some graph measures including clustering coefficient (C), global efficiency (Eglobal) and local efficiency (Elocal) on a millisecond timescale. Our results showed that the brain functional networks displayed rapid rearrangements of topological properties during transient feedback-related cognition process for both two groups. More importantly, we found that SZ patients exhibited significantly reduced theta-band (time window of 170-350 ms after stimuli onset) brain functional connectivity strength, Eglobal, Elocal and C in response to negative feedback stimuli compared to HC group. The network based statistic (NBS) analysis detected one significantly decreased theta-band subnetwork in SZ patients mainly involving in frontal-occipital and temporal-occipital connections compared to HC group. In addition, clozapine treatment seemed to greatly reduce theta-band power and topological measures of brain networks in SZ patients. Finally, the theta-band power, graph measures and functional connectivity were extracted to train a support vector machine classifier for classification of HC from SZ, or Cloz + SZ or Cloz- SZ, and a relatively good classification accuracy of 84.48%, 89.47% and 78.26% was obtained, respectively. The above results suggested a less optimal organization of theta-band brain network in SZ patients, and studying the topological parameters of brain networks evolve over time during transient feedback-related processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement learning deficits in SZ patients.