Engineering of a novel Saccharomyces cerevisiae wine strain with a respiratory phenotype at high external glucose concentrations

The recently described respiratory strain Saccharomyces cerevisiae KOY.TM6*P is, to our knowledge, the only reported strain of S. cerevisiae which completely redirects the flux of glucose from ethanol fermentation to respiration, even at high external glucose concentrations (27). In the KOY.TM6*P strain, portions of the genes encoding the predominant hexose transporter proteins, Hxt1 and Hxt7, were fused within the regions encoding transmembrane (TM) domain 6. The resulting chimeric gene, TM6*. encoded a chimera composed of the amino-terminal half of Hxt1 and the carboxy-terminal half of Hxt7. It was subsequently integrated into the genome of an hxt null strain. In this study, we have demonstrated the transferability of this respiratory phenotype to the V5 hxt1-7Δ strain, a derivative of a strain used in enology. We also show by using this mutant that it is not necessary to transform a complete hxt null strain with the TM6* construct to obtain a nonethanol-producing phenotype. The resulting V5.TM6*P strain, obtained by transformation of the V5 hxt1-7Δ strain with the TM6* chimeric gene, produced only minor amounts of ethanol when cultured on external glucose concentrations as high as 5%. Despite the fact that glucose flux was reduced to 30% in the V5.TM6*P strain compared with that of its parental strain, the V5.TM6*P strain produced biomass at a specific rate as high as 85% that of the V5 wild-type strain. Even more relevant for the potential use of such a strain for the production of heterologous proteins and also of low-alcohol beverages is the observation that the biomass yield increased 50% with the mutant compared to its parental strain. Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Respiratory function monitoring using a real-time three-dimensional fiber-optic shaping sensing scheme based upon fiber Bragg gratings

An array of in-line curvature sensors on a garment is used to monitor the thoracic and abdominal movements of a human during respiration. The results are used to obtain volumetric changes of the human torso in agreement with a spirometer used simultaneously at the mouth. The array of 40 in-line fiber Bragg gratings is used to produce 20 curvature sensors at different locations, each sensor consisting of two fiber Bragg gratings. The 20 curvature sensors and adjoining fiber are encapsulated into a low-temperature-cured synthetic silicone. The sensors are wavelength interrogated by a commercially available system from Moog Insensys, and the wavelength changes are calibrated to recover curvature. A three-dimensional algorithm is used to generate shape changes during respiration that allow the measurement of absolute volume changes at various sections of the torso. It is shown that the sensing scheme yields a volumetric error of 6%. Comparing the volume data obtained from the spirometer with the volume estimated with the synchronous data from the shape-sensing array yielded a correlation value 0.86 with a Pearson's correlation coefficient p <0.01.

The application of a long period grating sensor array scheme to human respiratory plethysmography

Preliminary results are given for a long period grating sensing array scheme based upon a derivative spectroscopy interrogation technique for Human Respiratory Plethysmography with simultaneous measurements of a spirometer, reasonable agreement with recorded volumetric changes was found.

Respiratory monitoring using fibre long period grating sensors

We demonstrate the use of a series of in-line fibre long period grating curvature sensors on a garment, used to monitor the thoracic and abdominal volumetric tidal movements of a human subject. These results are used to obtain volumetric tidal changes of the human torso showing reasonable agreement with a spirometer used simultaneously to record the volume at the mouth during breathing. The curvature sensors are based upon long period gratings written in a progressive three layered fibre that are insensitive to refractive index changes. The sensor platform consists of the long period grating laid upon a carbon fibre ribbon, which is encapsulated in a low temperature curing silicone rubber.

Application of long period gratings sensors to respiratory function monitoring

A series of in-line curvature sensors on a garment are used to monitor the thoracic and abdominal movements of a human during respiration. These results are used to obtain volumetric tidal changes of the human torso showing reasonable agreement with a spirometer used simultaneously to record the volume at the mouth during breathing. The curvature sensors are based upon long period gratings written in a progressive three layered fibre that are insensitive to refractive index changes. The sensor platform consists of the long period grating laid upon a carbon fibre ribbon, which is encapsulated in a low temperature curing silicone rubber. An array of sensors is also used to reconstruct the shape changes of a resuscitation manikin during simulated respiration. The data for reconstruction is obtained by two methods of multiplexing and interrogation: firstly using the transmission spectral profile of the LPG's attenuation bands measured using an optical spectrum analyser; secondly using a derivative spectroscopy technique.

The application of a long period grating array for respiratory function monitoring

In this paper, multiplexed sensor network capable of monitoring the shape changes of the torso for respiratory function monitoring is developed. As a demonstration, LPGs written into refractive index insensitive, progressive three layered fibre are embedded into supporting material is then placed on a resuscitation training manikin simulating respiration. A derivative spectroscopy interrogation technique is implemented and the bend sensitivity of the LPGs is used to reconstruct the shape of the manikin's torso. © 2003 IEEE.

Application of long-period-grating sensors to respiratory plethysmography

A series of in-line curvature sensors on a garment are used to monitor the thoracic and abdominal movements of a human during respiration. These results are used to obtain volumetric tidal changes of the human torso in agreement with a spirometer used simultaneously at the mouth. The curvature sensors are based on long-period gratings (LPGs) written in a progressive three-layered fiber to render the LPGs insensitive to the refractive index external to the fiber. A curvature sensor consists of the fiber long-period grating laid on a carbon fiber ribbon, which is then encapsulated in a low-temperature curing silicone rubber. The sensors have a spectral sensitivity to curvature, dλ/dR from ∼7-nm m to ∼9-nm m. The interrogation technique is borrowed from derivative spectroscopy and monitors the changes in the transmission spectral profile of the LPG's attenuation band due to curvature. The multiplexing of the sensors is achieved by spectrally matching a series of distributed feedback (DFB) lasers to the LPGs. The versatility of this sensing garment is confirmed by it being used on six other human subjects covering a wide range of body mass indices. Just six fully functional sensors are required to obtain a volumetric error of around 6%. © 2007 Society of Photo-Optical Instrumentation Engineers.

Motion correction algorithm of lung tumors for respiratory gated PET images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

Motion Correction Algorithm of Lung Tumors for Respiratory Gated PET Images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

Fuzzy angel: uma arquitetura distribuída de telemedicina para monitoramento de pacientes com esclerose lateral amiotrófica

The Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness that leads the patient to death, usually due to respiratory complications. Thus, as the disease progresses the patient will require noninvasive ventilation (NIV) and constant monitoring. This paper presents a distributed architecture for homecare monitoring of nocturnal NIV in patients with ALS. The implementation of this architecture used single board computers and mobile devices placed in patient’s homes, to display alert messages for caregivers and a web server for remote monitoring by the healthcare staff. The architecture used a software based on fuzzy logic and computer vision to capture data from a mechanical ventilator screen and generate alert messages with instructions for caregivers. The monitoring was performed on 29 patients for 7 con-tinuous hours daily during 5 days generating a total of 126000 samples for each variable monitored at a sampling rate of one sample per second. The system was evaluated regarding the rate of hits for character recognition and its correction through an algorithm for the detection and correction of errors. Furthermore, a healthcare team evaluated regarding the time intervals at which the alert messages were generated and the correctness of such messages. Thus, the system showed an average hit rate of 98.72%, and in the worst case 98.39%. As for the message to be generated, the system also agreed 100% to the overall assessment, and there was disagreement in only 2 cases with one of the physician evaluators.

What We Mean When We Talk About Adherence In Respiratory Medicine

Funding acknowledgements The Respiratory Effectiveness Group (REG; www.effectivenessevaluation.org) supported the Expert Adherence Panel Meeting at which many of the concepts presented in this paper were first discussed. REG also supported the manuscript submission costs. AD, EvG and MdB have received funding from the European Community’s 7th Framework (FP7/2007-2013) under grant agreement n°282593.

Enhancing Cloud Security and Privacy: The Power and the Weakness of the Audit Trail

Winner of best paper award.

What We Mean When We Talk About Adherence In Respiratory Medicine

Funding acknowledgements The Respiratory Effectiveness Group (REG; www.effectivenessevaluation.org) supported the Expert Adherence Panel Meeting at which many of the concepts presented in this paper were first discussed. REG also supported the manuscript submission costs. AD, EvG and MdB have received funding from the European Community’s 7th Framework (FP7/2007-2013) under grant agreement n°282593.