Cellular excitability and the regulation of functional neuronal identity: from gene expression to neuromodulation

The intrinsic properties of a neuron determine the translation of synaptic input to axonal output. It is this input– output relationship that is the heart of all nervous system activity. As such, the overall regulation of the intrinsic excitability of a neuron directly determines the output of that neuron at a given point in time, giving the cell a unique “functional identity.” To maintain this distinct functional output, neurons must adapt to changing patterns of synaptic excitation. These adaptations are essential to prevent neurons from either falling silent as synaptic excitation falls or becoming saturated as excitation increases. In the absence of stabilizing mechanisms, activity-dependent plasticity could drive neural activity to saturation or quiescence. Furthermore, as cells adapt to changing patterns of synaptic input, presumably the overall balance of intrinsic conductances of the cell must be maintained so that reliable output is achieved (Daoudal and Debanne, 2003; Turrigiano and Nelson, 2004; Frick and Johnston, 2005). Although these regulatory phenomena have been well documented, the molecular and physiological mechanisms involved are poorly understood

A Culturally Sensitive Social Support Intervention for Chinese American Breast Cancer Survivors (Joy Luck Academy): Protocol for a Randomized Controlled Trial

© Qian Lu, Krystal Warmoth, Lingjun Chen, Christine S Wu, Qiao Chu, Yisheng Li, Matthew W Gallagher, Annette L Stanton, Marjorie Kagawa Singer, Lucy Young, Alice Loh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/),BACKGROUND: Breast cancer is the most prevalent type of cancer among Asian American women. Chinese American immigrant breast cancer survivors face unique challenges because of cultural and socioecological factors. They report emotional distress and the need for social, emotional, and spiritual support. However, culturally and linguistically appropriate information for managing survivorship health care is often unavailable. OBJECTIVE: To improve the health outcomes for this underserved and understudied population, we developed, designed, and launched a randomized controlled trial to test the health benefits of a culturally sensitive social support intervention (Joy Luck Academy). In this paper, we describe the research protocol. METHODS: This randomized controlled trial will enroll Chinese-speaking, stage 0 to 3 breast cancer survivors who have completed treatment within the previous 36 months using a community-based participatory research approach. We will randomly assign 168 participants to the intervention or control group. The intervention arm will attend 7 weekly 3.5-hour peer mentor and educational sessions. The control group will receive the educational information. We will assess health outcomes at baseline, immediately after the Joy Luck Academy, and at 1- and 4-month follow-ups. The primary outcome is quality of life, as measured by the Functional Assessment of Cancer Therapy scale. Secondary outcomes include depressive symptoms, positive affect, fatigue, and perceived stress. We will also explore how the intervention influences cortisol levels. To identify how and to whom the program is effective, we will measure social and personal resources and theorized mechanisms and perform qualitative interviews with a subsample of participants to enhance the interpretation of quantitative data. RESULTS: Recruitment began in February 2015, and data collection was completed in February 2019. We expect to complete data management by August 2021 and publish results in 2022. CONCLUSIONS: If the Joy Luck Academy is demonstrated to be effective, it may be easily disseminated as an intervention for other groups of Asian American immigrant breast cancer survivors. Furthermore, similar programs could be integrated into other diverse communities.Peer reviewedFinal Published versio

Mass spectral characterization of peptide transmitters/hormones in the nervous system and neuroendocrine organs of the American lobster Homarus americanus

The American lobster Homarus americanus is a decapod crustacean with both high economic and scientific importance. To facilitate physiological investigations of peptide transmitter/hormone function in this species, we have used matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS), matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) and nanoscale liquid chromatography coupled to electrospray ionization quadrupole time-of-flight tandem mass spectrometry (nanoLC-ESI-Q-TOF-MS/MS) to elucidate the peptidome present in its nervous system and neuroendocrine organs. In total, 84 peptides were identified, including 27 previously known H. americanus peptides (e.g. VYRKPPFNGSIFamide [Val1-SIFamide]), 23 peptides characterized previously from other decapods, but new to the American lobster (e.g. pQTFQYSRGWTNamide [Arg7-corazonin]), and 34 new peptides de novo sequenced/detected for the first time in this study. Of particular note are a novel B-type allatostatin (TNWNKFQGSWamide) and several novel FMRFamide-related peptides, including an unsulfated analog of sulfakinin (GGGEYDDYGHLRFamide), two myosuppressins (QDLDHVFLRFamide and pQDLDHVFLRFamide), and a collection of short neuropeptide F isoforms (e.g. DTSTPALRLRFamide, and FEPSLRLRFamide). Our data also include the first detection of multiple tachykinin-related peptides in a non-brachyuran decapod, as well as the identification of potential individual-specific variants of orcokinin and orcomyotropin-related peptide. Taken collectively, our results not only expand greatly the number of known H. americanus neuropeptides, but also provide a framework for future studies on the physiological roles played by these molecules in this commercially and scientifically important species

Genomic Polymorphism of the Pandemic A (H1N1) Influenza Viruses Correlates with Viral Replication, Virulence, and Pathogenicity In Vitro and In Vivo

The novel pandemic A (H1N1) virus was first identified in Mexico in April 2009 and quickly spread worldwide. Like all influenzas, the H1N1 strain-specific properties of replication, virulence, and pathogenicity are a result of the particular genomic sequence and concerted expression of multiple genes. Thus, specific mutations may support increased virulence and may be useful as biomarkers of potential threat to human health. We performed comparative genomic analysis of ten strains of the 2009 pandemic A (H1N1) influenza viruses to determine whether genotypes associated with clinical phenotypes, which ranged from mild to severe illness and up to lethal. Virus replication capacity was tested for each strain in vitro using cultured epithelial cells, while virulence and pathogenicity were investigated in vivo using the BALB/c mouse model. The results indicated that A/Sichuan/1/2009 strain had significantly higher replication ability and virulence than the other strains, and five unique non-synonymous mutations were identified in important gene-encoding sequences. These mutations led to amino acid substitutions in HA (L32I), PA (A343T), PB1 (K353R and T566A), and PB2 (T471M), and may be critical molecular determinants for replication, virulence, and pathogenicity. Our results suggested that the replication capacity in vitro and virulence in vivo of the 2009 pandemic A (H1N1) viruses were not associated with the clinical phenotypes. This study offers new insights into the transmission and evolution of the 2009 pandemic A (H1N1) virus

Adaption of Seasonal H1N1 Influenza Virus in Mice

The experimental infection of a mouse lung with influenza A virus has proven to be an invaluable model for studying the mechanisms of viral adaptation and virulence. The mouse adaption of human influenza A virus can result in mutations in the HA and other proteins, which is associated with increased virulence in mouse lungs. In this study, a mouse-adapted seasonal H1N1 virus was obtained through serial lung-to-lung passages and had significantly increased virulence and pathogenicity in mice. Genetic analysis indicated that the increased virulence of the mouse-adapted virus was attributed to incremental acquisition of three mutations in the HA protein (T89I, N125T, and D221G). However, the mouse adaption of influenza A virus did not change the specificity and affinity of receptor binding and the pH-dependent membrane fusion of HA, as well as the in vitro replication in MDCK cells. Notably, infection with the mouse adapted virus induced severe lymphopenia and modulated cytokine and chemokine responses in mice. Apparently, mouse adaption of human influenza A virus may change the ability to replicate in mouse lungs, which induces strong immune responses and inflammation in mice. Therefore, our findings may provide new insights into understanding the mechanisms underlying the mouse adaption and pathogenicity of highly virulent influenza viruses

SAR target recognition based on model transfer and hinge loss with limited data

Abstract Convolutional neural networks have made great achievements in field of optical image classification during recent years. However, for Synthetic Aperture Radar automatic target recognition (SAR-ATR) tasks, the performance of deep learning networks is always degraded by the insufficient size of SAR images, which cause both severe over-fitting and low-capacity feature extraction model. On the other hand, models with high feature representation ability usually lose anti-overfitting capability to a certain extent, while enhancing the network’s robustness leads to degradation in feature extraction capability. To balance above both problems, a network with model transfer using the GAN-WP and non-greedy loss is introduced in this paper. Firstly, inspired by the Support Vector Machine’s mechanism, multi-hinge loss is used during training stage. Then, instead of directly training a deep neural network with the insufficient labeled SAR dataset, we pretrain the feature extraction network by an improved GAN, called Wasserstein GAN with gradient penalty and transfer the pre-trained layers to an all-convolutional network based on the fine-tune technique. Furthermore, experimental results on the MSTAR dataset illustrate the effectiveness of the proposed new method, which additional shows the classification accuracy can be improved more largely than other method in the case of sparse training dataset

Transferable discriminative feature mining for unsupervised domain adaptation

Abstract Unsupervised Domain Adaptation (UDA) aims to seek an effective model for unlabeled target domain by leveraging knowledge from a labeled source domain with a related but different distribution. Many existing approaches ignore the underlying discriminative features of the target data and the discrepancy of conditional distributions. To address these two issues simultaneously, the paper presents a Transferable Discriminative Feature Mining (TDFM) approach for UDA, which can naturally unify the mining of domain-invariant discriminative features and the alignment of class-wise features into one single framework. To be specific, to achieve the domain-invariant discriminative features, TDFM jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data. It then conducts the class-wise alignment by decreasing intra-class variations and increasing inter-class differences across domains, encouraging the emergence of transferable discriminative features. When combined, these two procedures are mutually beneficial. Comprehensive experiments verify that TDFM can obtain remarkable margins over state-of-the-art domain adaptation methods

Deep ladder-suppression network for unsupervised domain adaptation

Abstract Unsupervised domain adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. Most existing approaches learn domain-invariant features by adapting the entire information of the images. However, forcing adaptation of domain-specific variations undermines the effectiveness of the learned features. To address this problem, we propose a novel, yet elegant module, called the deep ladder-suppression network (DLSN), which is designed to better learn the cross-domain shared content by suppressing domain-specific variations. Our proposed DLSN is an autoencoder with lateral connections from the encoder to the decoder. By this design, the domain-specific details, which are only necessary for reconstructing the unlabeled target data, are directly fed to the decoder to complete the reconstruction task, relieving the pressure of learning domain-specific variations at the later layers of the shared encoder. As a result, DLSN allows the shared encoder to focus on learning cross-domain shared content and ignores the domain-specific variations. Notably, the proposed DLSN can be used as a standard module to be integrated with various existing UDA frameworks to further boost performance. Without whistles and bells, extensive experimental results on four gold-standard domain adaptation datasets, for example: 1) Digits; 2) Office31; 3) Office-Home; and 4) VisDA-C, demonstrate that the proposed DLSN can consistently and significantly improve the performance of various popular UDA frameworks

Joint clustering and discriminative feature alignment for unsupervised domain adaptation

Abstract Unsupervised Domain Adaptation (UDA) aims to learn a classifier for the unlabeled target domain by leveraging knowledge from a labeled source domain with a different but related distribution. Many existing approaches typically learn a domain-invariant representation space by directly matching the marginal distributions of the two domains. However, they ignore exploring the underlying discriminative features of the target data and align the cross-domain discriminative features, which may lead to suboptimal performance. To tackle these two issues simultaneously, this paper presents a Joint Clustering and Discriminative Feature Alignment (JCDFA) approach for UDA, which is capable of naturally unifying the mining of discriminative features and the alignment of class-discriminative features into one single framework. Specifically, in order to mine the intrinsic discriminative information of the unlabeled target data, JCDFA jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabeled target data, where the classification of the source domain can guide the clustering learning of the target domain to locate the object category. We then conduct the cross-domain discriminative feature alignment by separately optimizing two new metrics: 1) an extended supervised contrastive learning, i.e. , semi-supervised contrastive learning 2) an extended Maximum Mean Discrepancy (MMD), i.e. , conditional MMD, explicitly minimizing the intra-class dispersion and maximizing the inter-class compactness. When these two procedures, i.e. , discriminative features mining and alignment are integrated into one framework, they tend to benefit from each other to enhance the final performance from a cooperative learning perspective. Experiments are conducted on four real-world benchmarks ( e.g. , Office-31, ImageCLEF-DA, Office-Home and VisDA-C). All the results demonstrate that our JCDFA can obtain remarkable margins over state-of-the-art domain adaptation methods. Comprehensive ablation studies also verify the importance of each key component of our proposed algorithm and the effectiveness of combining two learning strategies into a framework