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Age-related macular degeneration (AMD) is a multifactorial disease, whose complete pathogenesis is still unclear. Local hemodynamics may play a crucial role in its manifestation and progression. To evaluate choroidal and retinal vascular parameters, a total of 134 eyes were analyzed, 100 with intermediate AMD and 34 age matched healthy controls. 131 eyes of 104 patients were eligible for complete image assessment and 3 eyes were excluded for insufficient image quality: Group 1: intermediate AMD (n = 97) and Group 2: healthy controls (n = 34). Spectral domain optic coherence tomography (SD-OCT) with enhanced depth imaging (EDI) and optic coherence tomography angiography (OCT-A) were acquired using Spectralis (Heidelberg Engineering). Choroid and retinal capillary plexus were evaluated and image binarization was used to obtain quantitative data. Mean age was 77.67 years old (YO) and 67.2% were women. Total subfoveal choroidal area and luminal area were significantly reduced in Group 1 compared with Group 2 (0.88 mm2 and 0.40 mm2 vs. 1.24 mm2 and 0.55 mm2, respectively) (p < 0.05). Regarding choriocapillary flow density, AMD eyes recorded reduced values (34.83%) compared with controls (36.25%) (p < 0.05). Chorioretinal vasculature is impaired in intermediate AMD patients and vascular parameters could be attractive new prognostic biomarkers. Future therapeutic approaches may target this vascular dysfunction and delay disease progression.

An array of anthropogenic pressures is affecting tropical ecosystems, posing major conservation challenges for scientists, stakeholders and populations. Illegal cyanide fishing is one of the major threats to Indo-Pacific coral reefs, targeting a multitude of colorful species for the marine aquarium trade as well as large-sized groupers and wrasses for the food fish trade. Ultimately, the continued use of this destructive practice as oceans warm may overload tropical ecosystems and result in irreversible ecological damage. Here we show that the impact of cyanide poisoning in an ornamental tropical marine fish is magnified under increased temperatures. A sole pulse exposure of 60 s to 50 mg L–1 of cyanide under current temperature (26°C) caused substantial mortality (50–100%) in eight species of Pomacentridae. The clownfish Amphiprion ocellaris was the most resistant, especially medium-sized fish [average total length and weight of 38 mm and 1.12 g; LC50 (95% CI) = 50.00 (46.76 − 53.24) mg L–1] that showed shorter recovery times and higher survival rates (%) when compared to small-sized ones [average total length and weight of 25 mm and 0.30 g; LC50 (95% CI) = 28.45 (20.17 − 36.72) mg L–1]. However, when the most resistant size-class was concomitantly exposed to a sub-lethal dosage of cyanide (25 mg L–1 instead of 50 mg L–1) and ocean warming scenarios for 2100 (+3°C and heat wave +6°C), survival rates (%) decreased to 60 and 20%, respectively, and recovery times increased in the worst case scenario. Mortality outbreaks, as well as vulnerability to predation, will likely expand in fish inhabiting coral reefs exposed to cyanide fishing unless stronger conservation measures are taken in tropical reefs to limit this destructive practice now and in the oceans of tomorrow.

Biofilm formation and drug resistance are two key pathogenesis traits exhibited by Candida glabrata as a human pathogen. Interestingly, specific pathways appear to be in the crossroad between the two phenomena, making them promising targets for drug development. In this study, the 10 multidrug resistance transporters of the Drug:H+ Antiporter family of C. glabrata were screened for a role in biofilm formation. Besides previously identified players in this process, namely CgTpo1_2 and CgQdr2, two others are shown to contribute to biofilm formation: CgDtr1 and CgTpo4. The deletion of each of these genes was found to lead to lower biofilm formation, in both SDB and RPMI media, while their expression was found to increase during biofilm development and to be controlled by the transcription factor CgTec1, a predicted key regulator of biofilm formation. Additionally, the deletion of CgDTR1, CgTPO4, or even CgQDR2 was found to increase plasma membrane potential and lead to decreased expression of adhesin encoding genes, particularly CgALS1 and CgEPA1, during biofilm formation. Although the exact role of these drug transporters in biofilm formation remains elusive, our current model suggests that their control over membrane potential by the transport of charged molecules, may affect the perception of nutrient availability, which in turn may delay the triggering of adhesion and biofilm formation.

X-linked choroideremia (CHM) is a disease characterized by gradual retinal degeneration caused by loss of the Rab Escort Protein, REP1. Despite partial compensation by REP2 the disease is characterized by prenylation defects in multiple members of the Rab protein family that are master regulators of membrane traffic. Remarkably, the eye is the only organ affected in CHM patients, possibly because of the huge membrane traffic burden of the post mitotic photoreceptors, which synthesise outer segments, and the adjacent retinal pigment epithelium that degrades the spent portions each day. In this study, we aimed to identify defects in membrane traffic that might lead to photoreceptor cell death in CHM. In a heterozygous null female mouse model of CHM (Chmnull/WT), degeneration of the photoreceptor layer was clearly evident from increased numbers of TUNEL positive cells compared to age matched controls, small numbers of cells exhibiting signs of mitochondrial stress and greatly increased microglial infiltration. However, most rod photoreceptors exhibited remarkably normal morphology with well-formed outer segments and no discernible accumulation of transport vesicles in the inner segment. The major evidence of membrane trafficking defects was a shortening of rod outer segments that was evident at 2 months of age but remained constant over the period during which the cells die. A decrease in rhodopsin density found in the outer segment may underlie the outer segment shortening but does not lead to rhodopsin accumulation in the inner segment. Our data argue against defects in rhodopsin transport or outer segment renewal as triggers of cell death in CHM.

Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 ± 0.2140 and 0.8750 ± 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model.

The prevalence of antifungal resistance in Candida glabrata, especially against azole drugs, results in difficult-to-treat and potentially life-threatening infections. Understanding the molecular basis of azole resistance in C. glabrata is crucial to designing more suitable therapeutic strategies. In this study, the role of the transcription factor encoded by ORF CAGL0B03421g, here denominated as CgMar1 (Multiple Azole Resistance 1), in azole susceptibility was explored. Using RNA-sequencing, CgMar1 was found to regulate 337 genes under fluconazole stress, including several related to lipid biosynthesis pathways. In this context, CgMar1 and its target CgRSB1, encoding a predicted sphingoid long-chain base efflux transporter, were found to contribute to plasma membrane sphingolipid incorporation and membrane permeability, decreasing fluconazole accumulation. CgMar1 was found to associate with the promoter of CgRSB1, which contains two instances of the CCCCTCC consensus, found to be required for CgRSB1 activation during fluconazole stress. Altogether, a regulatory pathway modulating azole susceptibility in C. glabrata is proposed, resulting from what appears to be a neofunctionalization of a Hap1-like transcription factor.

Semiconductor lasers can exhibit complex dynamical behavior in the presence of external perturbations. Delayed optical feedback, re-injecting part of the emitted light back into the laser cavity, in particular, can destabilize the laser’s emission. We focus on the emission properties of a semiconductor laser subject to such optical feedback, where the delay of the light re-injection is large compared to the relaxation oscillations period. We present an overview of the main dynamical features that emerge in semiconductor lasers subject to delayed optical feedback, emphasizing how to experimentally characterize these features using intensity and high-resolution optical spectra measurements. The characterization of the system requires the experimentalist to be able to simultaneously measure multiple time scales that can be up to six orders of magnitude apart, from the picosecond to the microsecond range. We highlight some experimental observations that are particularly interesting from the fundamental point of view and, moreover, provide opportunities for future photonic applications.

Steady-state methods have been widely used in Europe to analyse the energy performance of low-energy buildings. The accuracy of such methods depends on the assumptions regarding the compensation of non-stationary effects, but also on the input design data, such as the temperature of unconditioned spaces (UnSp). This temperature depends mainly on the thermal characteristics of UnSp envelope, air ventilation rate, temperature of the conditioned spaces and the external environment. External environment varies over time, daily and seasonally, making it difficult to accurately estimate UnSp temperature. European Union (EU) directives stated that the UnSp temperature can be evaluated by the adjustment factor (b) set by EN ISO 13789. However, each Member State may adjust procedures, by proposing simplified approaches, either for new or existing buildings. In this paper the b-values measured on-site in three dwellings were compared to those calculated by EN ISO 13789 as well as those estimated based on simplified procedures, allowed in the regulatory framework of some EU Member States, namely Ireland, Portugal, Spain, France and Italy. The study allowed to conclude that EN ISO 13789 and Irish BR 443 provided similar values. However, if the purpose is to simplify procedures and reduce computation effort, French RE2020 proved to be very effective. The thermal characteristics of the UnSp envelope and air ventilation rate were identified as the parameters that most affect the estimation of the b-value, while thermal losses through linear thermal bridges and the ground do not seem to have a significant impact.

En este trabajo se presenta un análisis económico de un proceso discontinuo cíclico para remoción de materia orgánica, nitrógeno y fósforo de efluentes líquidos mediante barros activados. El proceso es del tipo reactores discontinuos secuenciales con nitrificación externa. Se evalúan y comparan mediante simulación los costos operativos y de inversión de dos configuraciones de proceso alternativas para distintos caudales de diseño que cumplen con los límites de descarga exigidos. Se determinó la escala de tratamiento (caudal de diseño) que conduce al mínimo costo total anual específico para cada configuración estudiada. Desde el punto de vista económico, se concluye que es más conveniente para tratar grandes caudales de efluente instalar plantas pequeñas en paralelo operando al caudal nominal que determina el mínimo costo total anual específico de tratamiento que una planta de gran dimensión.

Este trabajo trata sobre la optimización del diseño del proceso de captura de CO2 por membranas a partir de gases generados en una planta de combustión de carbón. Diferentes configuraciones alternativas son embebidas simultáneamente en una superestructura a partir de la cual se deriva el modelo matemático que permite determinar la configuración óptima del proceso de separación, el área de membrana y consumo de potencia en cada etapa, y las correspondientes condiciones de operación. Precisamente, se propone minimizar el área total de membrana, adoptando como metas de diseño una recuperación de CO2 del 85.00% y pureza de 98.00% en la corriente enriquecida de CO2. Dicho problema se resolverá para dos tipos de flujos: contra-corriente y co-corriente. Las soluciones óptimas obtenidas para cada tipo de flujo son analizadas y comparadas en términos de la distribución de áreas, flujos, composiciones y la contribución individual de cada compresor al consumo total de potencia.

Electrified domestic hot water systems, being deferrable loads, are an important demand side management tool and thus have the potential to enhance photovoltaic self-consumption. This study addresses the energy and economic performance of photovoltaic self-consumption by using a typical Portuguese dwelling. Five system configurations were simulated: a gas boiler (with/without battery) and an electric boiler (without demand management and with genetic and heuristic optimization). A sensitivity analysis on photovoltaic capacity shows the optimum photovoltaic sizing to be in the range 1.0 to 2.5 kWp. The gas boiler scenario and the heuristic scenario present the best levelized cost of energy, respectively, for the lower and higher photovoltaic capacities. The use of a battery shows the highest levelized cost of energy and the heuristic scenario shows the highest solar fraction (56.9%). Results also highlight the great potential on increasing photovoltaic size when coupled with electrified domestic hot water systems, to accommodate higher solar fractions and achieve lower costs, through energy management.

Many cultural institutions lack an active policy to present to the public their collections, products and services. That is not the case of the Biblioteca Valenciana Nicolau Primitiu that in recent years has offered a wide range of guided tours (cultural, specialized, theatrical, virtual) to publicize both the library and the Monastery of San Miguel de los Reyes, building declared BIC in 2008 but located in a peripheral and degraded zone of Valencia. In the study, we analyze these visits, the results achieved and the possible improvements to include.

Early and intermediate AMD patients represent a heterogeneous population with an important but variable risk of progression to more advanced stages of the disease. The five-year progression from early and intermediate AMD to late disease is known to range from 0.4% to 53%. This wide variation explains the particular interest in searching predictive AMD biomarkers. Clinical parameters such as drusen size, presence of pigmentary abnormalities, and fellow eye status were, traditionally, the more important predictive elements. Multimodal retinal assessment (Color Fundus Photography, Optical Coherence Tomography, Optical Coherence Angiography and Fundus Autofluorescence) is providing new and accurate image biomarkers, useful in research and in daily practice. If individual progression risk could be anticipated, then management plans should be adapted accordingly, considering follow-up intervals and therapeutic interventions. Here, we reviewed the most important image progression biomarkers of early and intermediate AMD with relevant interest in clinical practice.

While complex lipids of seaweeds are known to display important phytochemical properties, their full potential is yet to be explored. This review summarizes the findings of a systematic survey of scientific publications spanning over the years 2000 to January 2021 retrieved from Web of Science (WoS) and Scopus databases to map the state of the art and identify knowledge gaps on the relationship between the complex lipids of seaweeds and their reported bioactivities. Eligible publications (270 in total) were classified in five categories according to the type of studies using seaweeds as raw biomass (category 1); studies using organic extracts (category 2); studies using organic extracts with identified complex lipids (category 3); studies of extracts enriched in isolated groups or classes of complex lipids (category 4); and studies of isolated complex lipids molecular species (category 5), organized by seaweed phyla and reported bioactivities. Studies that identified the molecular composition of these bioactive compounds in detail (29 in total) were selected and described according to their bioactivities (antitumor, anti-inflammatory, antimicrobial, and others). Overall, to date, the value for seaweeds in terms of health and wellness effects were found to be mostly based on empirical knowledge. Although lipids from seaweeds are little explored, the published work showed the potential of lipid extracts, fractions, and complex lipids from seaweeds as functional ingredients for the food and feed, cosmeceutical, and pharmaceutical industries. This knowledge will boost the use of the chemical diversity of seaweeds for innovative value-added products and new biotechnological applications.

Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this work, the properties of several ordinal-based quantifiers for the identification of time-delays from time series. To that end, we generate artificial time series of stochastic and deterministic time-delay models. We find that the presence of a nonlinearity in the generating model has consequences for the distribution of ordinal patterns and, consequently, on the delay-identification qualities of the quantifiers. Here, we put forward a novel ordinal-based quantifier that is particularly sensitive to nonlinearities in the generating model and compare it with previously-defined quantifiers. We conclude from our analysis on artificially generated data that the proper identification of the presence of a time-delay and its precise value from time series benefits from the complementary use of ordinal-based quantifiers and the standard autocorrelation function. We further validate these tools with a practical example on real-world data originating from the North Atlantic Oscillation weather phenomenon.

Atlantic salmon (Salmo salar) cage farming has traditionally been located at higher latitudes where cold seawater temperatures favor this practice. However, these regions can be impacted by ocean warming and heat waves that push seawater temperature beyond the thermo-tolerance limits of this species. As more mass mortality events are reported every year due to abnormal sea temperatures, the Atlantic salmon cage aquaculture industry acknowledges the need to adapt to a changing ocean. This paper reviews adult Atlantic salmon thermal tolerance limits, as well as the deleterious eco-physiological consequences of heat stress, with emphasis on how it negatively affects sea cage aquaculture production cycles. Biotechnological solutions targeting the phenotypic plasticity of Atlantic salmon and its genetic diversity, particularly that of its southernmost populations at the limit of its natural zoogeographic distribution, are discussed. Some of these solutions include selective breeding programs, which may play a key role in this quest for a more thermo-tolerant strain of Atlantic salmon that may help the cage aquaculture industry to adapt to climate uncertainties more rapidly, without compromising profitability. Omics technologies and precision breeding, along with cryopreservation breakthroughs, are also part of the available toolbox that includes other solutions that can allow cage farmers to continue to produce Atlantic salmon in the warmer waters of the oceans of tomorrow.

Abstract Soil contamination is a major hindrance for plant growth and development. The lack of effective strategies to remove chemicals released into the environment has raised the need to increase plant resilience to soil pollutants. Here, we investigated the ability of two Saccharomyces cerevisiae plasma-membrane transporters, the Major Facilitator Superfamily (MFS) member Tpo1p and the ATP-Binding Cassette (ABC) protein Pdr5p, to confer Multiple Drug Resistance (MDR) in Arabidopsis thaliana. Transgenic plants expressing either of the yeast transporters were undistinguishable from the wild type under control conditions, but displayed tolerance when challenged with the herbicides 2,4-D and barban. Plants expressing ScTPO1 were also more resistant to the herbicides alachlor and metolachlor as well as to the fungicide mancozeb and the Co2+, Cu2+, Ni2+, Al3+ and Cd2+ cations, while ScPDR5-expressing plants exhibited tolerance to cycloheximide. Yeast mutants lacking Tpo1p or Pdr5p showed increased sensitivity to most of the agents tested in plants. Our results demonstrate that the S. cerevisiae Tpo1p and Pdr5p transporters are able to mediate resistance to a broad range of compounds of agricultural interest in yeast as well as in Arabidopsis, underscoring their potential in future biotechnological applications.

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches.

Concentration-discharge relationships are a key tool for understanding the sources and transport of material from watersheds to fluvial networks. Storm events in particular provide insight into variability in the sources of solutes and sediment within watersheds, and the hydrologic pathways that connect hillslope to stream channel. Here we examine high-frequency sensor-based specific conductance and turbidity data from multiple storm events across two watersheds (Quebrada Sonadora and Rio Icacos) with different lithology in the Luquillo Mountains of Puerto Rico, a forested tropical ecosystem. Our analyses include Hurricane Maria, a category 5 hurricane. To analyze hysteresis, we used a recently developed set of metrics to describe and quantify storm events including the hysteresis index (HI), which describes the directionality of hysteresis loops, and the flushing index (FI), which can be used to infer whether the mobilization of material is source or transport limited. We also examine the role of antecedent discharge to predict hysteretic behavior during storms. Overall, specific conductance and turbidity showed contrasting responses to storms. The hysteretic behavior of specific conductance was similar across sites, displaying clockwise hysteresis and a negative FI indicating proximal sources of solutes and consistent source limitation. In contrast, the directionality of turbidity hysteresis was significantly different between watersheds, although both had strong flushing behavior indicative of transport limitation. Overall, models that included antecedent discharge did not perform any better than models with peak discharge alone, suggesting that the magnitude and trajectory of an individual event was the strongest driver of material flux and hysteretic behavior. Hurricane Maria produced unique hysteresis metrics within both watersheds, indicating a distinctive response to this major hydrological event. The similarity in response of specific conductance to storms suggests that solute sources and pathways are similar in the two watersheds. The divergence in behavior for turbidity suggests that sources and pathways of particulate matter vary between the two watersheds. The use of high-frequency sensor data allows the quantification of storm events while index-based metrics of hysteresis allow for the direct comparison of complex storm events across a heterogeneous landscape and variable flow conditions.

The mechanisms by which the pigment melanin is transferred from melanocytes and processed within keratinocytes to achieve skin pigmentation remain ill-characterized. Nevertheless, several models have emerged in the past decades to explain the transfer process. Here, we review the proposed models for melanin transfer in the skin epidermis, the available evidence supporting each one, and the recent observations in favor of the exo/phagocytosis and shed vesicles models. In order to reconcile the transfer models, we propose that different mechanisms could co-exist to sustain skin pigmentation under different conditions. We also discuss the limited knowledge about melanin processing within keratinocytes. Finally, we pinpoint new questions that ought to be addressed to solve the long-lasting quest for the understanding of how basal skin pigmentation is controlled. This knowledge will allow the emergence of new strategies to treat pigmentary disorders that cause a significant socio-economic burden to patients and healthcare systems worldwide and could also have relevant cosmetic applications.

Streams and rivers of the Luquillo Experimental Forest, Puerto Rico, have been the subject of extensive watershed and aquatic research since the 1980s. This research includes understanding stream export of nutrients and coarse particulate organic matter, physicochemical constituents, aquatic fauna populations and community structure. However, many of the streams and watersheds studied do not appear in standard scale maps. We document recent collaborative and multi-institutional work to improve hydrological network information and identify knowledge gaps. The methods used to delimit and densify stream networks include establishment and incorporation of an updated new vertical datum for Puerto Rico, LIDAR derived elevation, and a combination of visual-manual and automated digitalization processes. The outcomes of this collaborative effort have resulted in improved watershed delineation, densification of hydrologic networks to reflect the scale of on-going studies, and the identification of constraining factors such as unmapped roadways, culverts, and other features of the built environment that interrupt water flow and alter runoff pathways. This work contributes to enhanced knowledge for watershed management, including attributes of riparian areas, effects of road and channel intersections and ridge to reef initiatives with broad application to other watersheds.

Candida glabrata is an emerging fungal pathogen whose success depends on its ability to resist antifungal drugs but also to thrive against host defenses. In this study, the predicted multidrug transporter CgTpo4 (encoded by ORF CAGL0L10912g) is described as a new determinant of virulence in C. glabrata, using the infection model Galleria mellonella. The CgTPO4 gene was found to be required for the C. glabrata ability to kill G. mellonella. The transporter encoded by this gene is also necessary for antimicrobial peptide (AMP) resistance, specifically against histatin-5. Interestingly, G. mellonella’s AMP expression was found to be strongly activated in response to C. glabrata infection, suggesting AMPs are a key antifungal defense. CgTpo4 was also found to be a plasma membrane exporter of polyamines, especially spermidine, suggesting that CgTpo4 is able to export polyamines and AMPs, thus conferring resistance to both stress agents. Altogether, this study presents the polyamine exporter CgTpo4 as a determinant of C. glabrata virulence, which acts by protecting the yeast cells from the overexpression of AMPs, deployed as a host defense mechanism.

Tissue injury and inflammation may result in chronic pain, a severe debilitating disease that is associated with great impairment of quality of life. An increasing body of evidence indicates that members of the Rab family of small GTPases contribute to pain processing; however, their specific functions remain poorly understood. Here, we found using immunofluorescence staining and in situ hybridization that the small GTPase Rab27a is highly expressed in sensory neurons and in the superficial dorsal horn of the spinal cord of mice. Rab27a mutant mice, which carry a single-nucleotide missense mutation of Rab27a leading to the expression of a nonfunctional protein, show reduced mechanical hyperalgesia and spontaneous pain behavior in inflammatory pain models, while their responses to acute noxious mechanical and thermal stimuli is not affected. Our study uncovers a previously unrecognized function of Rab27a in the processing of persistent inflammatory pain in mice.

Saccharomyces boulardii is a probiotic yeast often used for the treatment of GI tract disorders such as diarrhea symptoms. It is genetically close to the model yeast Saccharomyces cerevisiae and its classification as a distinct species or a S. cerevisiae variant has long been discussed. Here, we review the main genetic divergencies between S. boulardii and S. cerevisiae as a strategy to uncover the ability to adapt to the host physiological conditions by the probiotic. S. boulardii does possess discernible phenotypic traits and physiological properties that underlie its success as probiotic, such as optimal growth temperature, resistance to the gastric environment and viability at low pH. Its probiotic activity has been elucidated as a conjunction of multiple pathways, ranging from improvement of gut barrier function, pathogen competitive exclusion, production of antimicrobial peptides, immune modulation, and trophic effects. This review summarizes the participation of S. boulardii in these mechanisms and the multifactorial nature by which this yeast modulates the host microbiome and intestinal function.

A system that combines a vapor compression refrigeration system (VCRS) with a vapor absorption refrigeration system (VARS) merges the advantages of both processes, resulting in a more cost-effective system. In such a cascade system, the electrical power for VCRS and the heat energy for VARS can be significantly reduced, resulting in a coefficient of performance (COP) value higher than the value of each system operating in standalone mode. A previously developed optimization model of a series flow double-effect H2O-LiBr VARS is extended to a superstructure-based optimization model to embed several possible configurations. This model is coupled to an R134a VCRS model. The problem consists in finding the optimal configuration of the cascade system and the sizes and operating conditions of all system components that minimize the total heat transfer area of the system, while satisfying given design specifications (evaporator temperature and refrigeration capacity of −17.0 °C and 50.0 kW, respectively), and using steam at 130 °C, by applying mathematical programming methods. The obtained configuration is different from those reported for combinations of double-effect H2O-LiBr VAR and VCR systems. The obtained optimal configuration is compared to the available data. The obtained total heat transfer area is around 7.3% smaller than that of the reference case.

This work focuses on the experimental data analysis of electroencephalography (EEG) data, in which multiple sensors are recording oscillatory voltage time series. The EEG data analyzed in this manuscript has been acquired using a low-cost commercial headset, the Emotiv EPOC+. Our goal is to compare different techniques for the optimal estimation of collective rhythms from EEG data. To this end, a traditional method such as the principal component analysis (PCA) is compared to more recent approaches to extract a collective rhythm from phase-synchronized data. Here, we extend the work by Schwabedal and Kantz (PRL 116, 104101 (2016)) evaluating the performance of the Kosambi-Hilbert torsion (KHT) method to extract a collective rhythm from multivariate oscillatory time series and compare it to results obtained from PCA. The KHT method takes advantage of the singular value decomposition algorithm and accounts for possible phase lags among different time series and allows to focus the analysis on a specific spectral band, optimally amplifying the signal-to-noise ratio of a common rhythm. We evaluate the performance of these methods for two particular sets of data: EEG data recorded with closed eyes and EEG data recorded while observing a screen flickering at 15 Hz. We found an improvement in the signal-to-noise ratio of the collective signal for the KHT over the PCA, particularly when random temporal shifts are added to the channels.

Finding interdependency relations between time series provides valuable knowledge about the processes that generated the signals. Information theory sets a natural framework for important classes of statistical dependencies. However, a reliable estimation from information-theoretic functionals is hampered when the dependency to be assessed is brief or evolves in time. Here, we show that these limitations can be partly alleviated when we have access to an ensemble of independent repetitions of the time series. In particular, we gear a data-efficient estimator of probability densities to make use of the full structure of trial-based measures. By doing so, we can obtain time-resolved estimates for a family of entropy combinations (including mutual information, transfer entropy and their conditional counterparts), which are more accurate than the simple average of individual estimates over trials. We show with simulated and real data generated by coupled electronic circuits that the proposed approach allows one to recover the time-resolved dynamics of the coupling between different subsystems.

Persistence and virulence of Candida glabrata infections are multifactorial phenomena, whose understanding is crucial to design more suitable therapeutic strategies. In this study, the putative multidrug transporter CgDtr1, encoded by ORF CAGL0M06281g, is identified as a determinant of C. glabrata virulence in the infection model Galleria mellonella. CgDTR1 deletion is shown to decrease the ability to kill G. mellonella larvae by decreasing C. glabrata ability to proliferate in G. mellonella hemolymph, and to tolerate the action of hemocytes. The possible role of CgDtr1 in the resistance to several stress factors that underlie death induced by phagocytosis was assessed. CgDTR1 was found to confer resistance to oxidative and acetic acid stress. Consistently, CgDtr1 was found to be a plasma membrane acetic acid exporter, relieving the stress induced upon C. glabrata cells within hemocytes, and thus enabling increased proliferation and virulence against G. mellonella larvae.

Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis. This is potentially relevant to patients without frequent or easily detectable spikes. To analyze the data, we use an up-to-date machine learning algorithm and explore the benefits of including different features obtained from the MEG data as inputs to the algorithm. We find that the relative power spectral density of the MEG time-series is sufficient to distinguish between healthy and epileptic subjects with a high prediction accuracy. We also find that a combination of features such as the phase-locked value and the relative power spectral density allow to discriminate generalized and focal epilepsy, when these features are calculated over a filtered version of the signals in certain frequency bands. Machine learning algorithms are currently being applied to the analysis and classification of brain signals. It is, however, less evident to identify the proper features of these signals that are prone to be used in such machine learning algorithms. Here, we evaluate the influence of the input feature selection on a clinical scenario to distinguish between healthy and epileptic subjects. Our results indicate that such distinction is possible with a high accuracy (86%), allowing the discrimination between idiopathic generalized and frontal focal epilepsy types.

Animal experimentation has a long history in the study of metabolic syndrome-related disorders. However, no consensus exists on the best models to study these syndromes. Knowing that different diets can precipitate different metabolic disease phenotypes, herein we characterized several hypercaloric rat models of obesity and type 2 diabetes, comparing each with a genetic model, with the aim of identifying the most appropriate model of metabolic disease. The effect of hypercaloric diets (high fat (HF), high sucrose (HSu), high fat plus high sucrose (HFHSu) and high fat plus streptozotocin (HF+STZ) during different exposure times (HF 3 weeks, HF 19 weeks, HSu 4 weeks, HSu 16 weeks, HFHSu 25 weeks, HF3 weeks + STZ) were compared with the Zucker fatty rat. Each model was evaluated for weight gain, fat mass, fasting plasma glucose, insulin and C-peptide, insulin sensitivity, glucose tolerance, lipid profile and liver lipid deposition, blood pressure, and autonomic nervous system function. All animal models presented with insulin resistance and dyslipidemia except the HF+STZ and HSu 4 weeks, which argues against the use of these models as metabolic syndrome models. Of the remaining animal models, a higher weight gain was exhibited by the Zucker fatty rat and wild type rats submitted to a HF diet for 19 weeks. We conclude that the latter model presents a phenotype most consistent with that observed in humans with metabolic disease, exhibiting the majority of the phenotypic features and comorbidities associated with type 2 diabetes in humans.

Candida albicans and Candida glabrata are the two most prevalent etiologic agents of candidiasis worldwide. Although both are recognized as pathogenic, their choice of virulence traits is highly divergent. Indeed, it appears that these different approaches to fungal virulence may be equally successful in causing human candidiasis. In this review, the virulence mechanisms employed by C. albicans and C. glabrata are analyzed, with emphasis on the differences between the two systems. Pathogenesis features considered in this paper include dimorphic growth, secreted enzymes and signaling molecules, and stress resistance mechanisms. The consequences of these traits in tissue invasion, biofilm formation, immune system evasion, and macrophage escape, in a species dependent manner, are discussed. This review highlights the observation that C. albicans and C. glabrata follow different paths leading to a similar outcome. It also highlights the lack of knowledge on some of the specific mechanisms underlying C. glabrata pathogenesis, which deserve future scrutiny.

Water–energy nexus has been recognized as an important and challenging issue, namely in industry. This is due to industry reforms, increasing demand, and climate change. This concept focuses on the link between energy and water infrastructure. Overall, there is limited understanding of the nature of this link, as it is assumed that water is not a threat to the energy sector or an influence of the electricity to the water resources. This work aims to present and evaluate case studies related to typical industrial water circuits. These circuits represent some of the most relevant industrial sectors in terms of water–energy nexus such as: steel industry, chemical industry, paper and pulp industry, and food industry. Moreover, these sectors also cover typical industrial water circuits, namely: cooling circuit, gas washing circuit, water treatment circuit, transportation circuit, and quenching circuit. The circuits have firstly been assembled in OpenModelica software considering the equipment and physical layout of each circuit. According to their actual operation conditions, the energy and water consumption have been estimated. Furthermore, water and energy efficiency improvement measures have been proposed and implemented into the assembled models. This enabled a techno-economic assessment based on the implementation of the improvement measures. In order to contextualise these results into the industrial trends, the achieved water and energy savings are projected into potential national and sectorial savings considering the current levels of water and energy demand for each sector.

The African continent faces several challenges and threats: high vulnerability to climate change, the fastest population increase, the lowest degree of electrification and the need for an energy transition towards renewable energies. Solar energy constitutes a viable option for addressing these issues. In a changing climate the efficient implementation of solar capacity should rely on comprehensive information about the solar resource. Here, the newest and highest resolution regional climate simulation results are used to project the future photovoltaic and concentrated solar power potentials for Africa. We show that the high potentials for solar energy will not be reduced much throughout Africa with climate change. However, the PV solar potential is projected to decrease up to about −10% in limited areas of eastern central Africa; increases are also projected to the northwest and southern Africa (up to about +5%). These changes are mostly determined by changes in solar irradiance but in certain areas the warming is a critical factor limiting PV potential.

Many documents associated with Count Fernán González are considered to be falsifications by a number of scholars, including the count’s donation to the monastery of San Sebastián de Silos, whose original has not come down to us. The present article sets out the authenticity of the document, above all by comparing its formulas with the Formulae Visigothicae and those of other, contemporary documents which indicate they were composed using the same formulary, adopted during the tenth and eleventh centuries over the whole of the kingdom of León.