Activity and Process Stability of Purified Green Pepper (Capsicum annuum) Pectin Methylesterase

Pectin methylesterase (PME) from green bell peppers (Capsicum annuum) was extracted and purified by affinity chromatography on a CNBr-Sepharose-PMEI column. A single protein peak with pectin methylesterase activity was observed. For the pepper PME, a biochemical characterization in terms of molar mass (MM), isoelectric points (pI), and kinetic parameters for activity and thermostability was performed. The optimum pH for PME activity at 22 °C was 7.5, and its optimum temperature at neutral pH was between 52.5 and 55.0 °C. The purified pepper PME required the presence of 0.13 M NaCl for optimum activity. Isothermal inactivation of purified pepper PME in 20 mM Tris buffer (pH 7.5) could be described by a fractional conversion model for lower temperatures (55?57 °C) and a biphasic model for higher temperatures (58?70 °C). The enzyme showed a stable behavior toward high-pressure/temperature treatments. Keywords: Capsicum annuum; pepper; pectin methylesterase; purification; characterization; thermal and high-pressure stabilit

Speech Processing and Prosody

International audienceThe prosody of the speech signal conveys information over the linguistic content of the message: prosody structures the utterance, and also brings information on speaker's attitude and speaker's emotion. Duration of sounds, energy and fundamental frequency are the prosodic features. However their automatic computation and usage are not obvious. Sound duration features are usually extracted from speech recognition results or from a force speech-text alignment. Although the resulting segmentation is usually acceptable on clean native speech data, performance degrades on noisy or not non-native speech. Many algorithms have been developed for computing the fundamental frequency, they lead to rather good performance on clean speech, but again, performance degrades in noisy conditions. However, in some applications, as for example in computer assisted language learning, the relevance of the prosodic features is critical; indeed, the quality of the diagnostic on the learner's pronunciation will heavily depend on the precision and reliability of the estimated prosodic parameters. The paper considers the computation of prosodic features, shows the limitations of automatic approaches, and discusses the problem of computing confidence measures on such features. Then the paper discusses the role of prosodic features and how they can be handled for automatic processing in some tasks such as the detection of discourse particles, the characterization of emotions, the classification of sentence modalities, as well as in computer assisted language learning and in expressive speech synthesis

What Do Human Parasites Do with a Chloroplast Anyway?

The malaria parasite has a chloroplast, which is a holdover from its algal past. The authors discuss the fascinating biology of this organelle and its promise for treatment in light of a seminal new study

Label-free cell separation and sorting in microfluidic systems

Cell separation and sorting are essential steps in cell biology research and in many diagnostic and therapeutic methods. Recently, there has been interest in methods which avoid the use of biochemical labels; numerous intrinsic biomarkers have been explored to identify cells including size, electrical polarizability, and hydrodynamic properties. This review highlights microfluidic techniques used for label-free discrimination and fractionation of cell populations. Microfluidic systems have been adopted to precisely handle single cells and interface with other tools for biochemical analysis. We analyzed many of these techniques, detailing their mode of separation, while concentrating on recent developments and evaluating their prospects for application. Furthermore, this was done from a perspective where inertial effects are considered important and general performance metrics were proposed which would ease comparison of reported technologies. Lastly, we assess the current state of these technologies and suggest directions which may make them more accessible

The I4U Mega Fusion and Collaboration for NIST Speaker Recognition Evaluation 2016

The 2016 speaker recognition evaluation (SRE'16) is the latest edition in the series of benchmarking events conducted by the National Institute of Standards and Technology (NIST). I4U is a joint entry to SRE'16 as the result from the collaboration and active exchange of information among researchers from sixteen Institutes and Universities across 4 continents. The joint submission and several of its 32 sub-systems were among top-performing systems. A lot of efforts have been devoted to two major challenges, namely, unlabeled training data and dataset shift from Switchboard-Mixer to the new Call My Net dataset. This paper summarizes the lessons learned, presents our shared view from the sixteen research groups on recent advances, major paradigm shift, and common tool chain used in speaker recognition as we have witnessed in SRE'16. More importantly, we look into the intriguing question of fusing a large ensemble of sub-systems and the potential benefit of large-scale collaboration.Peer reviewe

Thymoquinone Induces Telomere Shortening, DNA Damage and Apoptosis in Human Glioblastoma Cells

Background: A major concern of cancer chemotherapy is the side effects caused by the non-specific targeting of both normal and cancerous cells by therapeutic drugs. Much emphasis has been placed on discovering new compounds that target tumour cells more efficiently and selectively with minimal toxic effects on normal cells. Methodology/Principal Findings: The cytotoxic effect of thymoquinone, a component derived from the plant Nigella sativa, was tested on human glioblastoma and normal cells. Our findings demonstrated that glioblastoma cells were more sensitive to thymoquinone-induced antiproliferative effects. Thymoquinone induced DNA damage, cell cycle arrest and apoptosis in the glioblastoma cells. It was also observed that thymoquinone facilitated telomere attrition by inhibiting the activity of telomerase. In addition to these, we investigated the role of DNA-PKcs on thymoquinone mediated changes in telomere length. Telomeres in glioblastoma cells with DNA-PKcs were more sensitive to thymoquinone mediated effects as compared to those cells deficient in DNA-PKcs. Conclusions/Significance: Our results indicate that thymoquinone induces DNA damage, telomere attrition by inhibiting telomerase and cell death in glioblastoma cells. Telomere shortening was found to be dependent on the status of DNA-PKcs. Collectively, these data suggest that thymoquinone could be useful as a potential chemotherapeutic agent in th

Efficient Learning and Feature Selection in High Dimensional Regression

We present a novel algorithm for efficient learning and feature selection in high-dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version of the well-known statistical regression technique of backfitting. Using the expectation-maximization algorithm, along with variational approximation methods to overcome intractability, we extend our algorithm to include automatic relevance detection of the input features. This variational Bayesian least squares (VBLS) approach retains its simplicity as a linear model, but offers a novel statistically robust black-box approach to generalized linear regression with high-dimensional inputs. It can be easily extended to nonlinear regression and classification problems. In particular, we derive the framework of sparse Bayesian learning, the relevance vector machine, with VBLS at its core, offering significant computational and robustness advantages for this class of methods. The iterative nature of VBLS makes it most suitable for real-time incremental learning, which is crucial especially in the application domain of robotics, brain-machine interfaces, and neural prosthetics, where real-time learning of models for control is needed. We evaluate our algorithm on synthetic and neurophysiological data sets, as well as on standard regression and classification benchmark data sets, comparing it with other competitive statistical approaches and demonstrating its suitability as a drop-in replacement for other generalized linear regression techniques

Acute kidney injury in patients treated with immune checkpoint inhibitors

Background: Immune checkpoint inhibitor-associated acute kidney injury (ICPi-AKI) has emerged as an important toxicity among patients with cancer. Methods: We collected data on 429 patients with ICPi-AKI and 429 control patients who received ICPis contemporaneously but who did not develop ICPi-AKI from 30 sites in 10 countries. Multivariable logistic regression was used to identify predictors of ICPi-AKI and its recovery. A multivariable Cox model was used to estimate the effect of ICPi rechallenge versus no rechallenge on survival following ICPi-AKI. Results: ICPi-AKI occurred at a median of 16 weeks (IQR 8-32) following ICPi initiation. Lower baseline estimated glomerular filtration rate, proton pump inhibitor (PPI) use, and extrarenal immune-related adverse events (irAEs) were each associated with a higher risk of ICPi-AKI. Acute tubulointerstitial nephritis was the most common lesion on kidney biopsy (125/151 biopsied patients [82.7%]). Renal recovery occurred in 276 patients (64.3%) at a median of 7 weeks (IQR 3-10) following ICPi-AKI. Treatment with corticosteroids within 14 days following ICPi-AKI diagnosis was associated with higher odds of renal recovery (adjusted OR 2.64; 95% CI 1.58 to 4.41). Among patients treated with corticosteroids, early initiation of corticosteroids (within 3 days of ICPi-AKI) was associated with a higher odds of renal recovery compared with later initiation (more than 3 days following ICPi-AKI) (adjusted OR 2.09; 95% CI 1.16 to 3.79). Of 121 patients rechallenged, 20 (16.5%) developed recurrent ICPi-AKI. There was no difference in survival among patients rechallenged versus those not rechallenged following ICPi-AKI. Conclusions: Patients who developed ICPi-AKI were more likely to have impaired renal function at baseline, use a PPI, and have extrarenal irAEs. Two-thirds of patients had renal recovery following ICPi-AKI. Treatment with corticosteroids was associated with improved renal recovery

On the Origins of Suboptimality in Human Probabilistic Inference

Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior