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Abstract Background Efficient presentation of mutant peptide fragments by the human leukocyte antigen class I (HLA-I) genes is necessary for immune-mediated killing of cancer cells. According to recent reports, patient HLA-I genotypes can impact the efficacy of cancer immunotherapy, and the somatic loss of HLA-I heterozygosity has been established as a factor in immune evasion. While global deregulated expression of HLA-I has also been reported in different tumor types, the role of HLA-I allele-specific expression loss — that is, the preferential RNA expression loss of specific HLA-I alleles — has not been fully characterized in cancer. Methods Here, we use RNA and whole-exome sequencing data to quantify HLA-I allele-specific expression (ASE) in cancer using our novel method arcasHLA-quant. Results We show that HLA-I ASE loss in at least one of the three HLA-I genes is a pervasive phenomenon across TCGA tumor types. In pancreatic adenocarcinoma, tumor-specific HLA-I ASE loss is associated with decreased overall survival specifically in the basal-like subtype, a finding that we validated in an independent cohort through laser-capture microdissection. Additionally, we show that HLA-I ASE loss is associated with poor immunotherapy outcomes in metastatic melanoma through retrospective analyses. Conclusions Together, our results highlight the prevalence of HLA-I ASE loss and provide initial evidence of its clinical significance in cancer prognosis and immunotherapy treatment.

Currently, the most implemented solution for driving a pumping system (PS) at variable speed is using a variable frequency drive (VFD). Because most of the VFDs have integrated only one proportional-integral (PI) frequency controller whose parameters (proportional gain/the integration time) can be off-line tuned but cannot be changed during real-time operation, and many PS must operate in different regimes (at variable speed, variable flow rate, at variable pumping head or even at variable power, e.g., those powered by renewable energy sources), the adaptive control strategy proposed in this paper overcomes these problems with very good performances. The proposed adaptive control strategy uses only simple PI controllers for managing several operating regimes. The adaptive character is not ensured by re-tuning the PI controller parameters as in self-tuning controllers but by readjusting the control law through a change in the control loop depending on the controlled output of the process (pump speed, pump discharge, pumping head or absorbed power). The deviations of the mentioned controlled outputs from their referenced values are converted into electrical frequency error (the deviation of the current value from its reference), which is the regular input to the already tuned controller; therefore, no controller re-tuning is required.

Abstract Background The emergence of SARS-CoV-2 underscores the need to better understand the evolutionary processes that drive the emergence and adaptation of zoonotic viruses in humans. In the betacoronavirus genus, which also includes SARS-CoV and MERS-CoV, recombination frequently encompasses the receptor binding domain (RBD) of the Spike protein, which is responsible for viral binding to host cell receptors. In this work, we reconstruct the evolutionary events that have accompanied the emergence of SARS-CoV-2, with a special emphasis on the RBD and its adaptation for binding to its receptor, human ACE2. Methods By means of phylogenetic and recombination analyses, we found evidence of a recombination event in the RBD involving ancestral linages to both SARS-CoV and SARS-CoV-2. We then assessed the effect of this recombination at protein level by reconstructing the RBD of the closest ancestors to SARS-CoV-2, SARS-CoV, and other Sarbecoviruses, including the most recent common ancestor of the recombining clade. The resulting information was used to measure and compare, in silico, their ACE2-binding affinities using the physics-based trRosetta algorithm. Results We show that, through an ancestral recombination event, SARS-CoV and SARS-CoV-2 share an RBD sequence that includes two insertions (positions 432-436 and 460-472), as well as the variants 427N and 436Y. Both 427N and 436Y belong to a helix that interacts directly with the human ACE2 (hACE2) receptor. Reconstruction of ancestral states, combined with protein-binding affinity analyses, suggests that the recombination event involving ancestral strains of SARS-CoV and SARS-CoV-2 led to an increased affinity for hACE2 binding and that alleles 427N and 436Y significantly enhanced affinity as well. Conclusions We report an ancestral recombination event affecting the RBD of both SARS-CoV and SARS-CoV-2 that was associated with an increased binding affinity to hACE2. Structural modeling indicates that ancestors of SARS-CoV-2 may have acquired the ability to infect humans decades ago. The binding affinity with the human receptor would have been subsequently boosted in SARS-CoV and SARS-CoV-2 through further mutations in RBD.

In recent years, many pumping systems have begun to be powered by renewable energy generators, including mostly photovoltaic generators and, less frequently, electrical wind generators. Because of the technology’s complexity and novelty (it has not yet reached its maturation), most of those systems consist of single pumps powered by photovoltaic generators or electrical wind generators. For this reason, the current paper proposes a strategy for driving a multi-pump system with water storage powered by a fluctuating and intermittent power source, such as power grids, which are limited by price variation over different periods during the day, or photovoltaic generators and/or electrical wind generators. The current work begins by proposing a model of a multi-pump system with water storage, followed by the design of a control strategy for operating such a system powered by a fluctuating and intermittent power source in an energy-efficient manner, without sacrificing the reliability, robustness and lifetime of the plant. Finally, an analysis of two concrete situations encountered in practice is made: in one, the considered multi-pump system is powered only by a power grid limited by price variation over three periods; in the other, it is powered by a photovoltaic generator.

In recent years, the growth of environmental awareness has increased the interest in the development of biocomposites which are sustainable materials with an excellent price–performance ratio and low weight. The current study aimed to obtain and characterize the biocomposites prepared by thermoforming using coir fibres as reinforcing material and polypropylene as matrix. The biocomposites were produced with different coir fibres/polypropylene ratios and were characterized by physical–mechanical indices, thermal analysis, crystallinity, attenuated total reflection-Fourier transform infrared spectroscopy analysis (ATR-FTIR), scanning electron microscopy (SEM), and chromatic measurements. Both tensile and bending strength of biocomposites decreased when the coir fibre content increased. The melting temperature of biocomposite materials has decreased with the increase of the coir fibre loading. Regarding the thermal stability, the weight loss and degradation temperature increased with decreasing coir fibre content. The ATR-FTIR and SEM analyses underlined the modifications that took place in the structure of the biocomposites by modifying the coir fibres/matrix ratio.

Hemp fibres used as a reinforcing agent and three polymeric matrices (polypropylene, bicomponent, recycled polyester) were used to obtain composite materials by needle punching and heat pressing. The influence of the hemp/matrix ratio and the nature of the matrix on the properties of the composites were analysed. The obtained composites were characterised by physical–mechanical indices, thermal analysis (thermogravimetry (TG), differential thermogravimetry (DTG) and Differential Scanning Calorimetry (DSC)), Fourier Transform Infrared Spectroscopy (FTIR-ATR) analysis, Scanning Electron Microscopy (SEM) and Chromatic measurements. The mechanical properties of composites are influenced by both the hemp/matrix ratio and the nature of the matrix. The thermal stability of composites decreased as the amount of hemp increased (for the same mass losses, the decomposition temperature decreased significantly for composites containing a quantity of hemp greater than 50%). Regarding the nature of the matrix, for the same mass loss, the highest decomposition temperature was presented by the composites containing recycled polyester as matrix, and the lowest one was presented by composites containing polypropylene fibres as matrix. The FTIR and SEM analyses highlight the changes that occurred in the structure of the composite, changes determined both by the amount of hemp in the composite and by the nature of the matrix.

The starting point to design a minimum variance control law consists in identifying a linearized mathematical model (valid around an operating point) of a nonlinear process, respectively the on-line estimation of the parameters of this model. This paper presents a comparative study regarding the estimation of these parameters for the case when the process operates in open-loop, respectively the process is integrated into a closed-loop system specific to a minimum variance control. The comparison is made both analytically (for the general case) and through a validation study (by simulation) particularized for the case of an induction generator integrated into a wind energy conversion system. The main contribution of this paper consists in proving the fact that, in closed-loop (under the constraints imposed by the control law), the process parameters estimates differ from the real ones identified in open-loop (in free operating mode, without constraints). In addition, as a novelty, the paper demonstrates that, in steady-state, the process gain estimates are identical, both in closed-loop and open-loop, even though they are calculated based on different estimates of these linear model parameters. Thus, based on parameters estimates in closed-loop, the parameter estimator rather allows the estimation of the real process gain, although it does not accurately estimate the real values of the linearized model parameters (correctly estimated only in open-loop).

The present paper proposes (as the main contribution) an additional self-tuning mechanism for an adaptive minimum-variance control system, whose main goal is to extend its functionality for a large value range of unmeasurable perturbations which disturb the controlled process. Through the standard design procedure, a minimum variance controller uses by default an internal self-tuning mechanism based on the process parameter estimates. However, the main parameter which overwhelmingly influences the control performance is the control penalty factor ( ρ ) . This parameter weights the term that describes the control variance in a criterion function whose minimization is the starting point of the control law design. The classical minimum-variance control involves an off-line tuning of this parameter, its value being set as constant throughout the entire operating regime. Based on the measurement of the process output error, the contribution of the proposed strategy consists in a real-time tuning of the control penalty factor, to ensure the stability of the control system, even under conditions of high disturbances. The proposed tuning mechanism adjusts this parameter by implementing a bipositional switching strategy based on a sharp hysteresis loop. Therefore, instead of the standard solution that involves a constant value of the control penalty factor ρ (a priori computed and set), this paper proposes a dual value for this controller parameter. The main objective is to allow the controlled process to operate in a stable fashion even in more strongly disturbed regimes (regimes where the control system becomes unstable and is usually switched off for safety reasons). To validate the proposed strategy, an induction generator integrated into a wind energy conversion system was considered as controlled plant. Operating under the action of strong disturbances (wind gusts, electrical load variations), the extension of safe operating range (thus avoiding the system disengagement) is an important goal of such a control system.

ABSTRACT Most observation of human respiratory virus carriage is derived from medical surveillance; however, the infections documented by this surveillance represent only a symptomatic fraction of the total infected population. As the role of asymptomatic infection in respiratory virus transmission is still largely unknown and rates of asymptomatic shedding are not well constrained, it is important to obtain more-precise estimates through alternative sampling methods. We actively recruited participants from among visitors to a New York City tourist attraction. Nasopharyngeal swabs, demographics, and survey information on symptoms, medical history, and recent travel were obtained from 2,685 adults over two seasonal arms. We used multiplex PCR to test swab specimens for a selection of common respiratory viruses. A total of 6.2% of samples (168 individuals) tested positive for at least one virus, with 5.6% testing positive in the summer arm and 7.0% testing positive in the winter arm. Of these, 85 (50.6%) were positive for human rhinovirus (HRV), 65 (38.7%) for coronavirus (CoV), and 18 (10.2%) for other viruses (including adenovirus, human metapneumovirus, influenza virus, and parainfluenza virus). Depending on the definition of symptomatic infection, 65% to 97% of infections were classified as asymptomatic. The best-fit model for prediction of positivity across all viruses included a symptom severity score, Hispanic ethnicity data, and age category, though there were slight differences across the seasonal arms. Though having symptoms is predictive of virus positivity, there are high levels of asymptomatic respiratory virus shedding among the members of an ambulatory population in New York City. IMPORTANCE Respiratory viruses are common in human populations, causing significant levels of morbidity. Understanding the distribution of these viruses is critical for designing control methods. However, most data available are from medical records and thus predominantly represent symptomatic infections. Estimates for asymptomatic prevalence are sparse and span a broad range. In this study, we aimed to measure more precisely the proportion of infections that are asymptomatic in a general, ambulatory adult population. We recruited participants from a New York City tourist attraction and administered nasal swabs, testing them for adenovirus, coronavirus, human metapneumovirus, rhinovirus, influenza virus, respiratory syncytial virus, and parainfluenza virus. At recruitment, participants completed surveys on demographics and symptomology. Analysis of these data indicated that over 6% of participants tested positive for shedding of respiratory virus. While participants who tested positive were more likely to report symptoms than those who did not, over half of participants who tested positive were asymptomatic.

This paper describes a new methodology for increasing the security of analog data transmission based on a mixture of the original analog signal with one of the state variables of Rössler chaotic system. The method is described emphasizing on the practical implementation aspects.

This paper presents two self-tuning control structures synthesized through the minimization of two criterion functions. It is described the computation methodology of the control laws, both being particularized for the case of the synchronous generator's excitation control. The parameters estimator is considered the recursive least square error (RLSE) algorithm. In order to validate the considered control structures, two comparative study cases by computer simulation are presented.