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IntroductionSodium glucose cotransporter-2 (SGLT2) inhibitors are widely used for diabetes treatment. Although SGLT2 inhibitors have been clinically observed to increase food intake, roles or even the presence of SGLT2 in the central nervous system (CNS) has not been established. We aimed to elucidate potential functions of SGLT2 in the CNS, and the effects of CNS-targeted SGLT2 inhibitors on food intake.Research design and methodsWe administered three kinds of SGLT2 inhibitors, tofogliflozin, dapagliflozin, and empagliflozin, into the lateral ventricle (LV) in rats and evaluated their effects on food intake. We also evaluated the effects of tofogliflozin administration in the third (3V) and fourth ventricle (4V). Intraperitoneal administration of liraglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist known to suppress food intake, was combined with central tofogliflozin to elucidate whether GLP-1 signaling antagonizes the effect of central SGLT2 inhibitors on food intake. To elucidate potential molecular mechanisms mediating changes in feeding, hypothalamic areas associated with food intake regulation were harvested and analyzed after intracerebroventricular administration (ICV) of tofogliflozin.ResultsBolus ICV injection of tofogliflozin induced a robust increase in food intake starting at 1.5 hours postinjection, and lasting for 5 days. No effect was observed when the same dose of tofogliflozin was administered intraperitoneally. ICV dapagliflozin and empagliflozin significantly enhanced food intake, although the strength of these effects varied among drugs. Food intake was most markedly enhanced when tofogliflozin was infused into the LV. Fewer or no effects were observed with infusion into the 3V or 4V, respectively. Systemic administration of liraglutide suppressed the effect of ICV tofogliflozin on food intake. ICV tofogliflozin increased phosphorylation of AMPK and c-fos expression in the lateral hypothalamus.ConclusionsSGLT2 inhibitors in the CNS increase food intake. SGLT2 activity in the CNS may regulate food intake through AMPK phosphorylation in the lateral hypothalamic area.

The present study employed exploratory factor analysis in order to describe inter-relationships between different second language (L2) production measures and to find underlying constructs that affect them. Initially, 11 L2 production measures were considered for factor analysis; however, after examining factorability, the following five L2 speech production measures were used in the analysis: (1) S-nodes per T-unit; (2) Guiraud 2000; (3) percentage of error-free T-unit; (4) hesitation episodes per 10 seconds; and (5) silent pauses per 10 seconds. Since the purpose of the factor analysis was descriptive and exploratory as opposed to descriptive and inferential (e.g., Tinsley & Tinsley, 1987), a principal factor method was used to extract underlying factors, which were subsequently rotated using a promax method. Two underlying factors were identified, the former of which was loaded by S-nodes per T-unit, hesitation episodes per 10 seconds, and silent pauses per 10 seconds, and latter of which was loaded by Guiraud 2000 and percentage of error-free T-unit. After the interpretations of factor loadings and the structure matrix, the first factor was named as Speech Incessancy and the second factor Form Consciousness. Discussion includes appropriateness of the factor names, and limitations are addressed. Studies on task based language teaching (TBLT) has attracted increasing attention to the effect of manipulating task complexity on second language (L2) learning and performance. One of the necessary consequences of this line of research is the recognition of the importance of task performance assessment. In addition to active discussions on the methods of performance assessment (e.g., Brown, Hudson, Norris, & Bonk, 2002), methods to analyze and assess L2 performance in quantitative terms have also drawn considerable attention (e.g., Ellis & Barkhuizen, 200?). For instance, general categories of L2 production measures have been discussed; they include categories of complexity, accuracy, fluency and lexis (e.g., Skehan, 2009) as well as spoken/written and frequency/ratio/formulaic measures (e.g., Wolfe-Quintero, Inagaki, & Kim, 1998). Another issue is the distinction between general and specific measures. General measures are useful when comparing effects of different types of pedagogic tasks on L2 performance whereas specific measures are sensitive to cognitive demands tasks impose (e.g., Robinson, Cardieno, and Shirai, 2009). The categories of complexity, accuracy, and fluency (CAF) are further considered as multi-dimensional (e.g., Norris & Ortega, 2009) and dynamic (Larsen-Freeman, 2009), which evade simplification. There are of course other important issues, such as reliability, validity, and sensitivity of individual L2 production measures as well as inter-relationships between L2 production measures. The present study, among these important issues, will focus on relationships between L2 production measures of complexity, accuracy, and fluency. In order to investigate relationships between different L2 production measures, factor analysis has been used. Mehnert (1988) for example, using various L2 production measures, identified three underlying factors: complexity, accuracy and fluency. Skehan and Foster (2005) identified four factors: on-line planning, complexity, accuracy and breakdown fluency. Finally, Tavakoli and Skehan (2005) found three factors: temporal aspects of fluency, repair fluency, and language form (accuracy and complexity). The goal of the present study is to explore constructs underlying various L2 speech production measures. The purpose of the present study is exploratory and descriptive, not inferential. The database used in the present study is a sub-corpus, which was originally obtained for Ishikawa (2008, in preparation).

The development of a captioning system that supports the real-time understanding of monologue speech such as lectures and commentaries is required. In monologues, since a sentence tends to be long, each sentence is often displayed in multi lines on the screen. In the case, it is necessary to insert linefeeds into a text so that the text becomes easy to read. This paper proposes a rule-based technique for inserting linefeeds into a Japanese spoken monologue sentence as an elemental technique to generate the readable captions. Our method inserts linefeeds into a sentence by applying the rules based on morphemes, dependencies and clause boundaries. We established the rules by circumstantially investigating the corpus annotated with linefeeds. An experiment using Japanese monologue corpus has shown the effectiveness of our rules.

It is very significant to the knowledge society to accumulate spoken documents on the web. However, because of the high redundancy of spontaneous speech, the faithfully transcribed text is not readable on an Internet browser, and therefore not suitable as a web document. This paper proposes a technique for converting spoken documents into web documents for the purpose of building a speech archiving system. The technique edits automatically transcribed texts and improves their readability on the browser. The readable text can be generated by applying technology such as paraphrasing, segmentation, and structuring transcribed texts. Editing experiments using lecture data demonstrated the feasibility of the technique. A prototype system of spoken document archiving was implemented to confirm its effectiveness.

Spontaneously spoken Japanese includes a lot of grammatically ill-formed linguistic phenomena such as fillers, hesitations, inversions, and so on, which do not appear in written language. This paper proposes a novel method of robust dependency parsing using a large-scale spoken language corpus, and evaluates the availability and robustness of the method using spontaneously spoken dialogue sentences. By utilizing stochastic information about the appearance of ill-formed phenomena, the method can robustly parse spoken Japanese including fillers, inversions, or dependencies over utterance units. Experimental results reveal that the parsing accuracy reached 87.0 ％, and we confirmed that it is effective to utilize the location information of a bunsetsu, and the distance information between bunsetsus as stochastic information.

Spontaneously spoken Japanese includes a lot of grammatically ill-formed linguistic phenomena such as fillers, hesitations, inversions, and so on, which do not appear in written language. This paper proposes a method of robust dependency parsing using a large-scale spoken language corpus, and evaluates the availability and robustness of the method using spontaneously spoken dialogue sentences. By utilizing stochastic information about the appearance of ill-formed phenomena, the method can robustly parse spoken Japanese including fillers, inversions, or dependencies over utterance units. As a result of an experiment, the parsing accuracy provided 87.0%, and we confirmed that it is effective to utilize the location information of a bunsetsu, and the distance information between bunsetsus as stochastic information., Grant-in-Aids for Young Scientists of the Ministry of Education, Science, Sports and Culture, Japan;The Tatematsu Foundation

Spontaneous speech includes a broad range of linguistic phenomena characteristic of spoken language, and therefore a statistical approach would be effective for robust parsing of spoken language. Though a largescale syntactically annotated corpus is required for the stochastic parsing, its construction requires a lot of human resources. This paper proposes a method of efficiently constructing a spoken language corpus for which the dependency analysis is provided. This method uses an existing spoken language corpus. A stochastic dependency parse is employed to tag spoken language sentences with the dependency structures, and the results are corrected manually. The tagged corpus is constructed in a spiral fashion where in the corrected data is utilized as the statistical information for automatic parsing of other data. Taking this spiral approach reduces the parsing errors, also allowing us to reduce the correction cost. An experiment using 10,995 Japanese utterances shows the spiral approach to be effective for efficient corpus construction.