Cyclic AMP Increases Cell Surface Expression of Functional Na,K-ATPase Units in Mammalian Cortical Collecting Duct Principal Cells

Cyclic AMP (cAMP) stimulates the transport of Na+ and Na,K-ATPase activity in the renal cortical collecting duct (CCD). The aim of this study was to investigate the mechanism whereby cAMP stimulates the Na,K-ATPase activity in microdissected rat CCDs and cultured mouse mpkCCDc14 collecting duct cells. db-cAMP (10−3 M) stimulated by 2-fold the activity of Na,K-ATPase from rat CCDs as well as the ouabain-sensitive component of 86Rb+ uptake by rat CCDs (1.7-fold) and cultured mouse CCD cells (1.5-fold). Pretreatment of rat CCDs with saponin increased the total Na,K-ATPase activity without further stimulation by db-cAMP. Western blotting performed after a biotinylation procedure revealed that db-cAMP increased the amount of Na,K-ATPase at the cell surface in both intact rat CCDs (1.7-fold) and cultured cells (1.3-fold), and that this increase was not related to changes in Na,K-ATPase internalization. Brefeldin A and low temperature (20°C) prevented both the db-cAMP-dependent increase in cell surface expression and activity of Na,K-ATPase in both intact rat CCDs and cultured cells. Pretreatment with the intracellular Ca2+ chelator bis-(o-aminophenoxy)-N,N,N′,N′-tetraacetic acid also blunted the increment in cell surface expression and activity of Na,K-ATPase caused by db-cAMP. In conclusion, these results strongly suggest that the cAMP-dependent stimulation of Na,K-ATPase activity in CCD results from the translocation of active pump units from an intracellular compartment to the plasma membrane.

Urea-inducible Egr-1 transcription in renal inner medullary collecting duct (mIMCD3) cells is mediated by extracellular signal-regulated kinase activation.

Urea (200-400 milliosmolar) activates transcription, translation of, and trans-activation by the immediate-early gene transcription factor Egr-1 in a renal epithelial cell-specific fashion. The effect at the transcriptional level has been attributed to multiple serum response elements and their adjacent Ets motifs located within the Egr-1 promoter. Elk-1, a principal ternary complex factor and Ets domain-containing protein, is a substrate of the extracellular signal-regulated kinase (ERK) mitogen-activated protein kinases. In the renal medullary mIMCD3 cell line, urea (200-400 milliosmolar) activated both ERK1 and ERK2 as determined by in-gel kinase assay and immune-complex kinase assay of epitope-tagged] ERK1 and ERK2. Importantly, urea did not affect abundance of either ERK. Urea-inducible Egr-1 transcription was a consequence of ERK activation because the ERK-specific inhibitor, PD98059, abrogated transcription from the murine Egr-1 promoter in a luciferase reported gene assay. In addition, activators of protein kinase A, including forskolin and 8-Br-cAMP, which are known to inhibit ERK-mediated events, also inhibited urea-inducible Egr-1 transcription. Furthermore, urea-inducible activation of the physiological ERK substrate and transcription factor, Elk-1, was demonstrated through transient cotransfection of a chimeric Elk-1/GAL4 expression plasmid and a GAL4-driven luciferase reporter plasmid. Taken together, these data indicate that, in mIMCD3 cells, urea activates ERKs and the ERK substrate, Elk-1, and that ERK inhibition abrogates urea-inducible Egr-1 transcription. These data are consistent with a model of urea-inducible renal medullary gene expression wherein sequential activation of ERKs and Elk-1 results in increased transcription of Egr-1 through serum response element/Ets motifs.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Scientific bases of human-machine communication by voice.

The scientific bases for human-machine communication by voice are in the fields of psychology, linguistics, acoustics, signal processing, computer science, and integrated circuit technology. The purpose of this paper is to highlight the basic scientific and technological issues in human-machine communication by voice and to point out areas of future research opportunity. The discussion is organized around the following major issues in implementing human-machine voice communication systems: (i) hardware/software implementation of the system, (ii) speech synthesis for voice output, (iii) speech recognition and understanding for voice input, and (iv) usability factors related to how humans interact with machines.

The role of voice input for human-machine communication.

Optimism is growing that the near future will witness rapid growth in human-computer interaction using voice. System prototypes have recently been built that demonstrate speaker-independent real-time speech recognition, and understanding of naturally spoken utterances with vocabularies of 1000 to 2000 words, and larger. Already, computer manufacturers are building speech recognition subsystems into their new product lines. However, before this technology can be broadly useful, a substantial knowledge base is needed about human spoken language and performance during computer-based spoken interaction. This paper reviews application areas in which spoken interaction can play a significant role, assesses potential benefits of spoken interaction with machines, and compares voice with other modalities of human-computer interaction. It also discusses information that will be needed to build a firm empirical foundation for the design of future spoken and multimodal interfaces. Finally, it argues for a more systematic and scientific approach to investigating spoken input and performance with future language technology.

Military and government applications of human-machine communication by voice.

This paper describes a range of opportunities for military and government applications of human-machine communication by voice, based on visits and contacts with numerous user organizations in the United States. The applications include some that appear to be feasible by careful integration of current state-of-the-art technology and others that will require a varying mix of advances in speech technology and in integration of the technology into applications environments. Applications that are described include (1) speech recognition and synthesis for mobile command and control; (2) speech processing for a portable multifunction soldier's computer; (3) speech- and language-based technology for naval combat team tactical training; (4) speech technology for command and control on a carrier flight deck; (5) control of auxiliary systems, and alert and warning generation, in fighter aircraft and helicopters; and (6) voice check-in, report entry, and communication for law enforcement agents or special forces. A phased approach for transfer of the technology into applications is advocated, where integration of applications systems is pursued in parallel with advanced research to meet future needs.

Deployment of human-machine dialogue systems.

The deployment of systems for human-to-machine communication by voice requires overcoming a variety of obstacles that affect the speech-processing technologies. Problems encountered in the field might include variation in speaking style, acoustic noise, ambiguity of language, or confusion on the part of the speaker. The diversity of these practical problems encountered in the "real world" leads to the perceived gap between laboratory and "real-world" performance. To answer the question "What applications can speech technology support today?" the concept of the "degree of difficulty" of an application is introduced. The degree of difficulty depends not only on the demands placed on the speech recognition and speech synthesis technologies but also on the expectations of the user of the system. Experience has shown that deployment of effective speech communication systems requires an iterative process. This paper discusses general deployment principles, which are illustrated by several examples of human-machine communication systems.

Hypertonicity activates nonselective cation channels in mouse cortical collecting duct cells.

We investigated the effect of cell shrinkage on whole-cell currents of M-1 mouse cortical collecting duct cells. Addition of 100 mM sucrose to an isotonic NaCl bath solution induced cell shrinkage and increased whole-cell currents within 5-10 min by approximately 12-fold. The effect was reversible upon return to isotonic solution and could also be elicited by adding 100 mM urea or 50 mM NaCl. Replacement of bath Na+ by K+, Cs+, Li+, or Rb+ did not significantly affect the stimulated inward current, but replacement by N-methyl-D-glucamine reduced it by 88.1 +/- 1.3% (n = 34); this demonstrates that hypertonicity activates a nonselective alkali cation conductance. The activation was independent of extra- and intracellular Ca2+, but 1 or 10 mM ATP in the pipette suppressed it in a concentration-dependent manner, indicating that intracellular ATP levels may modulate the degree of channel activation. Flufenamic acid (0.1 mM) and gadolinium (0.1 mM) inhibited the stimulated current by 68.7 +/- 5.9% (n = 9) and 32.4 +/- 11.7% (n = 6), respectively, whereas 0.1 mM amiloride had no significant effect. During the early phase of hypertonic stimulation single-channel transitions could be detected in whole-cell current recordings, and a gradual activation of 30 and more individual channels with a single-channel conductance of 26.7 +/- 0.4 pS (n = 29) could be resolved. Thus, we identified the nonselective cation channel underlying the shrinkage-induced whole-cell conductance that may play a role in volume regulation.

Calix[6]arene Derivatives as Pivotal Components of Working Devices and Molecular Machine Prototypes

This Ph.D. thesis describes the synthesis, characterization and study of calix[6]arene derivatives as pivotal components for the construction of molecular machine prototypes. Initially, the ability of a calix[6]arene wheel to supramolecularly assist and increase the rate of a nucleophilic substitution reaction was exploited for the synthesis of two constitutionally isomeric oriented rotaxanes. Then, the synthesis and characterization of several hetero-functionalised calix[6]arene derivatives and the possibility to obtain molecular muscle prototypes was reported. The ability of calix[6]arenes to form oriented pseudorotaxane towards dialkyl viologen axles was then exploited for the synthesis of two calixarene-based [2]catenanes. As last part of this thesis, studies on the electrochemical response of the threading-dethreading process of calix[6]arene-based pseudorotaxanes and rotaxanes supported on glassy carbon electrodes are reported.

Machine Translation: Translators' Friend or Foe?

This paper discusses the impact of machine translation on the language industry, specifically addressing its effect on translators. It summarizes the history of the development of machine translation, explains the underlying theory that ties machine translation to its practical applications, and describes the different types of machine translation as well as other tools familiar to translators. There are arguments for and against its use, as well as evaluation methods for testing it. Internet and real-time communication are featured for their role in the increase of machine translation use. The potential that this technology has in the future of professional translation is examined. This paper shows that machine translation will continue to be increasingly used whether translators like it or not.

Machine learning techniques for automatic opinion detection in non-traditional textual genres

This paper presents a preliminary study in which Machine Learning experiments applied to Opinion Mining in blogs have been carried out. We created and annotated a blog corpus in Spanish using EmotiBlog. We evaluated the utility of the features labelled firstly carrying out experiments with combinations of them and secondly using the feature selection techniques, we also deal with several problems, such as the noisy character of the input texts, the small size of the training set, the granularity of the annotation scheme and the language object of our study, Spanish, with less resource than English. We obtained promising results considering that it is a preliminary study.

The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced

Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).