[Establishment of a CFTR-based detection method for the second messenger cAMP in the cytoplasm].

Objective: To establish a detection method based on Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) that can sensitively detect the second messenger cyclic AMP (cAMP) in the cytoplasm. Methods: The eukaryotic expression vectors of CFTR and YFP-H148Q / I152L were constructed respectively. FRT cells co-expressing CFTR and YFP-H148Q / I152L were obtained by liposome transfection. The expression of CFTR and YFP-H148Q / I152L in FRT cells was observed by an inverted fluorescence microscopy, and flow cytometry was used to detect the purity of cells; The cell model was identified by the fluorescence quenching kinetics test. The validation of the cell model which could screen CFTR modulators was verified by the fluorescence quenching kinetics experiments. The radioimmunoassay was used to detect the cAMP concentration in cytoplasm after adding CFTR activator. Results: The results of the inverted fluorescence microscope showed that CFTR was expressed in the cell membrane and YFP-H148Q / I152L was expressed in the cytoplasm of FRT cells. The FRT cell model stably co-expressing ANO1 and YFP-H148Q / I152L was successfully constructed. The model could screen CFTR modulators, and the slope of fluorescence change and the concentration of CFTR modulators were in a dose-dependent manner. The slope of the fluorescence could reflect the cAMP concentration in the cytoplasm. The cell model could sensitively detect the intracellular cAMP concentration. Conclusion: The cell model could efficiently and sensitively detect the second messenger cAMP concentration in the cytoplasm, and it provided a simple and efficient method for the study of other targets associated cAMP signal.

On the similarities of representations in artificial and brain neural networks for speech recognition.

Introduction: In recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance, despite their huge differences in implementation, and so deep learning models can-in principle-serve as candidates for mechanistic models of the human auditory system. Methods: Utilizing high-performance automatic speech recognition systems, and advanced non-invasive human neuroimaging technology such as magnetoencephalography and multivariate pattern-information analysis, the current study aimed to relate machine-learned representations of speech to recorded human brain representations of the same speech. Results: In one direction, we found a quasi-hierarchical functional organization in human auditory cortex qualitatively matched with the hidden layers of deep artificial neural networks trained as part of an automatic speech recognizer. In the reverse direction, we modified the hidden layer organization of the artificial neural network based on neural activation patterns in human brains. The result was a substantial improvement in word recognition accuracy and learned speech representations. Discussion: We have demonstrated that artificial and brain neural networks can be mutually informative in the domain of speech recognition.

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.

[Pilot study of treating syphilitic uveitis with vitrectomy--one case report].

PURPOSE: to study the possibility of treating syphilitic uveitis with vitrectomy, and observe its safety as well as validity. METHOD: case observation and literature review. RESULTS: one bilateral syphilitic uveitis was treated with anti-inflammation and anti-syphilis treatment. The visual acuity at left side improved dramatically during short time while left eye kept same without improving. After carefully following up and analyzed for the unfavorable recovery, standard three-incision vitrectomy was performed. The visual acuity at the right eye improved quickly and even exceeded the left eye within half month reaching to 0.9 from 0.02. CONCLUSION: Vitrectomy is a useful tool, at least in certain case, in treating syphilitic uveitis. Its safety and mechanism need further amply study, though there is no any obvious side effect in the reported case.