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

Museum resilience: the impact of the Covid-19 pandemic on the independent museums in the UK

Since the coronavirus swept across the globe, the cultural and creative sectors worldwide have faced unprecedented challenges in terms of financial revenue, public safety and staff wellbeing, and museums are no exception. Therefore, the impact of Covid-19 pandemic on the museums and strategies for navigating these institutions within such a shifting social environment has been the central concern within the UK museum sector. This thesis scrutinizes the impact of the Covid-19 pandemic on independent museums in the UK and explores how the independent museum sector has responded to these challenges in the light of museum resilience. This thesis delves into both the conceptual underpinnings and practical applications of the concept of museum resilience in the context of the independent museum sector during the Covid-19 pandemic from 2020 to 2023. To capture data relating to the research question, this thesis has employed a multi-method qualitative research methodology, incorporating methods of secondary research, surveys, case studies, and semi- structured interviews. With the assistance of the Association of Independent Museums, this research received 207 survey responses from museum professionals across the independent museum sector in the UK. Additionally, thirteen distinctive independent museums participated as case study museums. Inspired by the conceptual roots of resilience in ecological literature, this research adopts an ecosystem framework to analyse the research data. This ecosystem comprises diverse stakeholders such as independent museums, governmental bodies, professional organizations, and museum communities. This thesis evaluates the functioning of each stakeholder within this ecosystem during the Covid-19 pandemic, investigating their interconnections. As a result, it furnishes a comprehensive perspective for understanding the dynamics of the independent museum sector in the context of the Covid-19 pandemic. In doing so, this research yields insights into the impact of the Covid-19 pandemic on independent museums and their responses, encompassing financial, institutional, and emotional dimensions. First, it details the impact of the Covid-19 pandemic on the financial stability of independent museums and then it explores the combined effect of financial pressure on institutional decision-making regarding operations, staffing, and long-term strategy. It also discerns the emotional impact of the Covid-19 pandemic on museum workers, such as worries, loneliness, and exhaustion. More importantly, this thesis provides numerous examples of efforts made by the independent museum sector to mitigate the impact of the pandemic, which involve a bundle of strategies to boost museum finances, re-engage museum audiences, and alleviate staff emotional stress. By analysing these responsive strategies, this thesis examines the notion of museum resilience within the context of the crisis induced by the Covid-19 pandemic, offering a more detailed interpretation of what it means for a museum to be resilient. This thesis reveals that the lockdown caused by Covid-19 pandemic significantly reduced ticket sales, retail, and catering revenues for many independent museums in the UK. Although visitor numbers gradually recovered as museums reopened, these institutions continued to face financial pressure to enhance online services and ensure on-site safety in the post-pandemic era. Thanks to the function of public grants, the financial impact of the pandemic did not lead to widespread permanent closures of independent museums in the UK. However, the loss of volunteers, the gap of digitalization between different museums, and the dilemma between promoting staff welfare and navigating financial constraints could pose future survival risks for these museums. Additionally, the pandemic took a toll on museum staff’s mental health, leading to anxiety, loneliness, and fatigue, which may present potential challenges in the future. Moreover, external risks such as climate change, geopolitical instability, and inflation further exacerbate the challenges faced by the independent museum sector in the UK. Therefore, enhancing museum resilience has become more critical than ever. This study, through analysing various cases, posits that museum resilience is a combination of the defensive ability to avoid destructive failure, consistency in upholding the museum’s core mission, the flexibility to mobilize resources, and the progressive power to achieve greater goals. This research provides a forward-looking perspective for future museum management studies, as the threat of the Covid-19 pandemic may have diminished, but potential museum crises have not disappeared. Beyond its focus on museology, this research presents successful crisis response strategies, serving as a resource for professionals in the cultural sector. These professionals can enhance their understanding and draw lessons from the experiences of independent museums

SAR Target Recognition based on Model Transfer and Hinge Loss with Limited Data

AbstractConvolutional 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.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

HiPSC-derived cardiac tissue for disease modeling and drug discovery

Li, J.; Hua, Y.; Miyagawa, S.; Zhang, J.; Li, L.; Liu, L.; Sawa, Y. hiPSC-Derived Cardiac Tissue for Disease Modeling and Drug Discovery. Int. J. Mol. Sci. 2020, 21, 8893

Systematic optimization for production of the anti-MRSA antibiotics WAP-8294A in an engineered strain of Lysobacter enzymogenes

WAP-8294A is a group of cyclic lipodepsipeptides and considered as the first-in-class new chemical entity with potent activity against methicillin-resistant Staphylococcus aureus. One of the roadblocks in developing the WAP-8294A antibiotics is the very low yield in Lysobacter. Here, we carried out a systematic investigation of the nutritional and environmental conditions in an engineered L. enzymogenes strain for the optimal production of WAP-8294A. We developed an activity-based simple method for quick screening of various factors, which enabled us to optimize the culture conditions. With the method, we were able to improve the WAP-8294A yield by 10-fold in small-scale cultures and approximately 15-fold in scale-up fermentation. Additionally, we found the ratio of WAP-8294A2 to WAP-8294A1 in the strains could be manipulated through medium optimization. The development of a practical method for yield improvement in Lysobacter will facilitate the ongoing basic research and clinical studies to develop WAP- 8294A into true therapeutics

Transferable Discriminative Feature Mining For Unsupervised Domain Adaptation

AbstractUnsupervised 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.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