Bowman-Birk inhibitors in lentil: Heterologous expression,functional characterisation and anti-proliferative properties in human colon cancer cells

A full-length cDNA, encoding a Bowman-Birk protease inhibitor (BBI), was isolated from lentil immature seeds. The deduced amino acid sequence was longer than that of the BBI extracted from lentil seeds and contained two binding sites; the first inhibitory site inhibits trypsin whereas the second one inhibits chymotrypsin. In order to characterize this lentil BBI, a longer (complete) and its C-terminally processed (mature) form were heterologously expressed in the yeast Pichia pastoris. The recombinant BBI proteins proved to be active against trypsin and chymotrypsin, showing K_i values at nanomolar levels. Mass spectrometry analysis revealed that complete BBI was composed of an array of molecular masses, whereas mature BBI showed the presence of a major peak of the expected size. The effects of mature BBI on the growth of human colon adenocarcinoma HT29 and colonic fibroblast CCD-18Co cells were evaluated. Lentil BBI was able to inhibit the growth of such cells at concentrations higher than 19 μM, in a concentration-dependent manner; by contrast, the CCD18-Co cells were unaffected. These data broaden our knowledge of the beneficial biological activities of naturally-occurring BBI proteins and address the need for systematic evaluation of natural variants in order to design novel strategies in preventive medicine.     

Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model Improvement

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.     

A voltage tunable CMOS differential active resistor and its application

We propose a novel CMOS differential voltage-controlled equivalent active resistor based on a modified common-source cross-coupled pair. The equivalent resistance exhibited by the circuit can be tuned to be either positive or negative through the tail current, which is controlled by a bias voltage. The complete cell comprises four transistors, two resistors in addition to the bias voltage, and tail current source. Application to the design of oscillators is demonstrated by theoretical analysis, circuit simulations, and verified experimentally through measurements on prototypes implemented using discrete transistors.     

Empirical Likelihood for Outlier Detection and Estimation in Autoregressive Time Series

Identification and estimation of outliers in time series is proposed by using empirical likelihood methods. Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types. Some other useful outlier types are considered as well. A simulation experiment is used for studying the behaviour of the empirical likelihood‐based method in finite samples and indicates that the proposed methods are preferable when dealing with the non‐Gaussian data. Our simulations suggest that the usual sequential procedure for multiple outlier detection is suitable also for the methods based on empirical likelihood.     

Sparse decomposition and modeling of anatomical shape variation

Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counterparts if constructed carefully. In most medical applications, models are required to have both good statistical performance and a relevant clinical interpretation to be of value. Morphometry of the corpus callosum is one illustrative example. This paper presents a method for relating spatial features to clinical outcome data. A set of parsimonious variables is extracted using sparse principal component analysis, producing simple yet characteristic features. The relation of these variables with clinical data is then established using a regression model. The result may be visualized as patterns of anatomical variation related to clinical outcome. In the present application, landmark-based shape data of the corpus callosum is analyzed in relation to age, gender, and clinical tests of walking speed and verbal fluency. To put the data-driven sparse principal component method into perspective, we consider two alternative techniques, one where features are derived using a model-based wavelet approach, and one where the original variables are regressed directly on the outcome.     

Analytic phase margin design

Doyle et al. (1992) presented an algorithm for analytic phase margin control design. Without special care, however, the compensator computed with this algorithm may not be a real rational function, The problem is evident when the plant has real unstable poles. In this case the algorithm requires a mapping of real points into complex values, and it is not clear that the resulting compensator has real coefficients. The purpose of this paper is to show how a complex mapping required in this algorithm can always be selected so that the compensator does have real coefficients     

A new technique for calibration in frequency of piezoelectric transducers

The calibration in frequency of piezoelectric transducers can be obtained by sending an ultrashort laser pulse (30 ps), generated by a mode-locked Nd:YAG laser, directly on the detector surface. Thanks to its short duration, the mode-locked pulse behaves as an excitation source of wide spectral content, so that the spectral analysis of the recorded electric signal gives direct information about the frequency response of the probe. In particular, we report a series of measurements on various transducers of several nominal frequencies and we show the different response of the probe to a focused laser beam to that of an unfocused one.     

Degradation and annealing of amorphous silicon solar cells by current injection: experiment and modeling

In this paper we report in detail on the effect of current injection in amorphous silicon solar cells. A set of devices has been degraded and then annealed at different current intensities. Device performances during the whole experiment have been monitored by current–voltage characteristics and quantum efficiency curves. It has been found that annealing rate increases with current intensity, while stabilized photovoltaic parameters decrease. Time evolution of efficiency and short-circuit current during degradation has been reproduced by a numerical device modeling, resulting in a pronounced increase of defects near the p–i interface. The model also demonstrated that annealing results are not well reproduced if current-induced annealing is not energy selective.     

The Role of Endothelium in COVID-19

The 2019 novel coronavirus, known as severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) or coronavirus disease 2019 (COVID-19), is causing a global pandemic. The virus primarily affects the upper and lower respiratory tracts and raises the risk of a variety of non-pulmonary consequences, the most severe and possibly fatal of which are cardiovascular problems. Data show that almost one-third of the patients with a moderate or severe form of COVID-19 had preexisting cardiovascular comorbidities such as diabetes mellitus, obesity, hypertension, heart failure, or coronary artery disease. SARS-CoV2 causes hyper inflammation, hypoxia, apoptosis, and a renin–angiotensin system imbalance in a variety of cell types, primarily endothelial cells. Profound endothelial dysfunction associated with COVID-19 can be the cause of impaired organ perfusion that may generate acute myocardial injury, renal failure, and a procoagulant state resulting in thromboembolic events. We discuss the most recent results on the involvement of endothelial dysfunction in the pathogenesis of COVID-19 in patients with cardiometabolic diseases in this review. We also provide insights on treatments that may reduce the severity of this viral infection.