Evidence for Surface Effects on the Intermolecular Interactions in Fe (II) Spin Crossover Coordination Polymers

From X-ray absorption spectroscopy (XAS) and X-ray photoemission spectroscopy (XPS) it is evident that the spin state transition behavior of Fe(II) spin crossover coordination polymer crystallites at the surface differs from the bulk. A comparison of four different coordination polymers reveals that the observed surface properties may differ from bulk for a variety of reasons. There are Fe(II) spin crossover coordination polymers with either almost complete switching of the spin state at the surface or no switching at all. Oxidation, differences in surface packing, and changes in coordination could all contribute to making the surface very different from the bulk. Some Fe(II) spin crossover coordination polymers may be sufficiently photoactive so that X-ray spectroscopies cannot discern the spin state transition

Constitutively active CaMKII Drives B lineage acute lymphoblastic leukemia/lymphoma in tp53 mutant zebrafish.

Acute lymphoblastic leukemia/lymphoma (ALL) is the most common pediatric cancer and is a malignancy of T or B lineage lymphoblasts. Dysregulation of intracellular Ca2+ levels has been observed in patients with ALL, leading to improper activation of downstream signaling. Here we describe a new zebrafish model of B ALL, generated by expressing human constitutively active CaMKII (CA-CaMKII) in tp53 mutant lymphocytes. In this model, B cell hyperplasia in the kidney marrow and spleen progresses to overt leukemia/lymphoma, with only 29% of zebrafish surviving the first year of life. Leukemic fish have reduced productive genomic VDJ recombination in addition to reduced expression and improper splicing of ikaros1, a gene often deleted or mutated in patients with B ALL. Inhibiting CaMKII in human pre-B ALL cells induced cell death, further supporting a role for CaMKII in leukemogenesis. This research provides novel insight into the role of Ca2+-directed signaling in lymphoid malignancy and will be useful in understanding disease development and progression

An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals

Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called ATtention-based LSTM with Domain Discriminator (ATDD-LSTM), a model based on Long Short-Term Memory (LSTM) for emotion recognition that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation

An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals

Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this paper, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called ATtention-based LSTM with Domain Discriminator (ATDD-LSTM) that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation.</p

Berberine Prevents Disease Progression of Nonalcoholic Steatohepatitis through Modulating Multiple Pathways

Background and Aims: The disease progression of nonalcoholic fatty liver disease (NAFLD) from simple steatosis (NAFL) to nonalcoholic steatohepatitis (NASH) is driven by multiple factors. Berberine (BBR) is an ancient Chinese medicine and has various beneficial effects on metabolic diseases, including NAFLD/NASH. However, the underlying mechanisms remain incompletely understood due to the limitation of the NASH animal models used. Methods: A high-fat and high-fructose diet-induced mouse model of NAFLD, the best available preclinical NASH mouse model, was used. RNAseq, histological, and metabolic pathway analyses were used to identify the potential signaling pathways modulated by BBR. LC–MS was used to measure bile acid levels in the serum and liver. The real-time RT-PCR and Western blot analysis were used to validate the RNAseq data. Results: BBR not only significantly reduced hepatic lipid accumulation by modulating fatty acid synthesis and metabolism but also restored the bile acid homeostasis by targeting multiple pathways. In addition, BBR markedly inhibited inflammation by reducing immune cell infiltration and inhibition of neutrophil activation and inflammatory gene expression. Furthermore, BBR was able to inhibit hepatic fibrosis by modulating the expression of multiple genes involved in hepatic stellate cell activation and cholangiocyte proliferation. Consistent with our previous findings, BBR’s beneficial effects are linked with the downregulation of microRNA34a and long noncoding RNA H19, which are two important players in promoting NASH progression and liver fibrosis. Conclusion: BBR is a promising therapeutic agent for NASH by targeting multiple pathways. These results provide a strong foundation for a future clinical investigation

Berberine Prevents Disease Progression of Nonalcoholic Steatohepatitis through Modulating Multiple Pathways

Background and Aims: The disease progression of nonalcoholic fatty liver disease (NAFLD) from simple steatosis (NAFL) to nonalcoholic steatohepatitis (NASH) is driven by multiple factors. Berberine (BBR) is an ancient Chinese medicine and has various beneficial effects on metabolic diseases, including NAFLD/NASH. However, the underlying mechanisms remain incompletely understood due to the limitation of the NASH animal models used. Methods: A high-fat and high-fructose diet-induced mouse model of NAFLD, the best available preclinical NASH mouse model, was used. RNAseq, histological, and metabolic pathway analyses were used to identify the potential signaling pathways modulated by BBR. LC–MS was used to measure bile acid levels in the serum and liver. The real-time RT-PCR and Western blot analysis were used to validate the RNAseq data. Results: BBR not only significantly reduced hepatic lipid accumulation by modulating fatty acid synthesis and metabolism but also restored the bile acid homeostasis by targeting multiple pathways. In addition, BBR markedly inhibited inflammation by reducing immune cell infiltration and inhibition of neutrophil activation and inflammatory gene expression. Furthermore, BBR was able to inhibit hepatic fibrosis by modulating the expression of multiple genes involved in hepatic stellate cell activation and cholangiocyte proliferation. Consistent with our previous findings, BBR’s beneficial effects are linked with the downregulation of microRNA34a and long noncoding RNA H19, which are two important players in promoting NASH progression and liver fibrosis. Conclusion: BBR is a promising therapeutic agent for NASH by targeting multiple pathways. These results provide a strong foundation for a future clinical investigation