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PurposeRecent literature has highlighted the overlapping contribution of demographic characteristics and spatial factors to urban-rural disparities in SARS-CoV-2 transmission and outcomes. Yet the interplay between individual characteristics, hospitalisation, and spatial factors for urban-rural disparities in COVID-19 mortality have received limited attention.MethodsTo fill this gap, we use national surveillance data collected by the European Centre for Disease Prevention and Control and we fit a generalized linear model to estimate the association between COVID-19 mortality and the individuals' age, sex, hospitalisation status, population density, share of the population over the age of 60, and pandemic wave across urban, intermediate and rural territories.FindingsWe find that in what type of territory individuals live (urban-intermediate-rural) accounts for a significant difference in their probability of dying given SARS-COV-2 infection. Hospitalisation has a large and positive effect on the probability of dying given SARS-CoV-2 infection, but with a gradient across urban, intermediate and rural territories. For those living in rural areas, the risk of dying is lower than in urban areas but only if hospitalisation was not needed; while for those who were hospitalised in rural areas the risk of dying was higher than in urban areas.ConclusionsTogether with individuals' demographic characteristics (notably age), hospitalisation has the largest effect on urban-rural disparities in COVID-19 mortality net of other individual and regional characteristics, including population density and the share of the population over 60.

Abstract We extend a previously developed epidemiological model for West Nile virus (WNV) infection in humans in Greece, employing laboratory-confirmed WNV cases and mosquito-specific characteristics of transmission, such as host selection and temperature-dependent transmission of the virus. Host selection was defined by bird host selection and human host selection, the latter accounting only for the fraction of humans that develop symptoms after the virus is acquired. To model the role of temperature on virus transmission, we considered five temperature intervals (≤ 19.25 °C; > 19.25 and 26.75 °C). The capacity of the new model to fit human cases and the week of first case occurrence was compared with the original model and showed improved performance. The model was also used to infer further quantities of interest, such as the force of infection for different temperatures as well as mosquito and bird abundances. Our results indicate that the inclusion of mosquito-specific characteristics in epidemiological models of mosquito-borne diseases leads to improved modelling capacity.

Introduction and Objectives: Alcohol-associated hepatitis (AH) is a severe entity associated with high mortality. Corticosteroids might be used in cases with severe disease and several dynamic models can predict mortality and response to corticosteroids in AH patients. However, there is no consensus on the best of them. This study aimed to evaluate dynamic models to predict response to corticosteroid treatment based on short-term mortality in patients with severe AH based on a worldwide cohort. Materials and Methods: A retrospective cohort study of patients with severe AH (between 2009 – 2019). We included patients who received corticosteroid treatment and calculated the Lille model of day 4 (Lille-4), day 7 (Lille-7) (cut-off value ≥0.45), and the Trajectory of Serum Bilirubin (TSB)(cut-off value ≥0.8 of the ratio between bilirubin at admission and day 7) to predict mortality. We estimated up to 30-day survival using Kaplan-Meier curves, and we performed multivariable analyzes using Cox regression. Specifically, we constructed two models to compare Lille-4 vs. TSB and Lille-7 vs. TSB, adjusting by well-known clinical variables associated with higher mortality in AH (age, sex, and creatinine at admission). Results: 1,066 patients were included (30 centers, 10 countries), age 47.7 ± 10.9 years, 30% women. The MELD score on admission was 25 [21-30]. Responders were considered by Lille-4 49.1%, Lille-7 46.6%, and TSB 55.4%. In the first Cox regression, we observed that Lille-4 and TSB predicted 30-day mortality (HR 3.0, 95%CI: 1.7-5.1; p

Introduction and Objectives: Severe alcohol-associated hepatitis (AH) is an entity with high morbidity and mortality; however, data in Latin America is limited. We aimed to characterize patients hospitalized for AH in a multinational cohort in Latin America. Materials and Methods: Multicenter prospective cohort study. We included patients admitted with severe AH between 2015-2022. Sociodemographic and clinical information was recorded. The analysis included survival analysis using Kaplan-Meier curves. This study was approved by the institutional ethics committee. Results: 470 patients from 24 centers (8 countries: Mexico, Chile, Argentina, Brazil, Peru, Bolivia, Colombia, and Ecuador) were included. Age 49.8 ± 10.6 years, 85.6% of men and 45% had a previous diagnosis of cirrhosis. Median MELD at admission was 26.9 [22-32] points. 26.5% met SIRS criteria and 34.3% had an acute kidney injury (AKI) on admission. Only 36.8% of patients were treated with corticosteroids. Survival at 30 days was 75.0% (95%CI: 70.1-79.3%) and 62.8% (95%CI: 57.1-68.0%) at 90 days. A total of 191 (45.8%) patients presented infections, 31.4% at admission and 24.9% during hospitalization. The most frequent locations of community-acquired infections were respiratory (33.5%), urinary (32.1%), spontaneous bacterial peritonitis (14.9%), and skin (10.5%), while the most frequent pathogens were Escherichia coli (40%), Klebsiella pneumoniae (12%), and Enterococcus (6%). The presence of infection at admission was associated with a decreased survival at 90-days (66.9% versus 48.1%, p=0.0002). AKI at admission was also associated with decreased survival at 90-days (86.8% versus 51.3%, p

Over the past few years, technologies in the field of computer vision have greatly advanced. The use of deep neural networks, together with the development of computing capabilities, has made it possible to solve problems of great interest to society. In this work, we focus on one such problem that has seen a great development, the recognition of actions in live videos. Although the problem has been oriented in different ways in the literature, we have focused on indoor residential environments, such as a house or a nursing home. Our system can be used to understand what actions a person or group of people are carrying out. Two of the approaches used to solve the problem have been 3D convolution networks and recurrent networks. In our case, we have created a model that accurately combines several recurrent networks with processed data from different techniques: image feature extraction, object detection and people’s skeletons. The need to integrate these three techniques arises from the search to improve the detection of certain actions by taking advantage of the best recognition offered by each of the methods. In a complete experimentation, where several techniques have been evaluated against different datasets, the classification of the actions has been improved with respect to the existing models.

BackgroundTo date, no specific therapy or vaccination is available for West Nile virus (WNV) infections in humans; preventive strategies represent the only possibility to control transmission. To focus these strategies, detailed knowledge of the virus dynamics is of paramount importance. However, several aspects of WNV transmission are still unclear, especially regarding the role of potential vertebrate host species. Whereas mosquitoes' intrinsic characteristics cause them to favour certain hosts (host preference), absolute selection is impossible in natural settings. Conversely, the selection carried out among available hosts and influenced from hosts' availability and other ecological/environmental factors is defined as host selection.Methodology/principal findingsIn July 2022, we searched PubMed database for original articles exploring host selection among WNV-transmitting Culex mosquitoes, the main WNV vector. We considered only original field studies estimating and reporting forage ratio. This index results from the ratio between the proportion of blood meals taken by mosquitoes on potential host species and the hosts' relative abundance. From the originally retrieved 585 articles, 9 matched the inclusion criteria and were included in this review. All but one of the included studies were conducted in the Americas, six in the United States, and one each in Mexico and Colombia. The remaining study was conducted in Italy. American Robin, Northern Cardinal, and House Finch were the most significantly preferred birds in the Americas, Common Blackbird in Italy.Conclusions/significanceAlthough ornithophilic, all observed WNV-transmitting mosquitoes presented opportunistic feeding behaviour. All the observed species showed potential to act as bridges for zoonotic diseases, feeding also on humans. All the observed mosquitoes presented host selection patterns and did not feed on hosts as expected by chance alone. The articles observe different species of mosquitoes in different environments. In addition, the way the relative host abundance was determined differed. Finally, this review is not systematic. Therefore, the translation of our results to different settings should be conducted cautiously.

This paper describes a prototype of artificial olfactory system or electro-optical nose (NEONOSE) containing commercially available MOX and optical (NDIR) gas sensors, forming an array of a total of 13 sensors (six MOX and seven NDIR sensors). The advantage of this configuration is to combine non-specific (MOX) and specific (NDIR) sensors to obtain an orthogonal array of sensors with a wider spectrum of responses. All the electronic circuits have been designed for this application. The prototype consists of two boards (containing different type of sensors) with two microcontrollers that communicates each other by using a digital interface. The Main Board is responsible for collecting the measurements from all the sensors and transmitting them via Bluetooth to a Smartphone. All the sensors that transmit data via I2C bus, which are all the MOX sensors and one of the optical sensors, are located on this board (main). The Secondary Board contains other NDIR sensors and is responsible for processing and sending this data to the Main Board via SPI communication. All the sensors of the Secondary Board are NDIR type. Some of them return the values in analog format, requiring the use of operational amplifiers for signal processing and analog-to-digital converters for data generation. The pre-processed data of all the sensors are sent via Bluetooth to an Android smartphone, where they are collected and stored using an own-developed application. Some measurements with different industrial gases have been done for testing the device. Keywords: metal-oxide sensors (MOX), optical sensors, electronic nose, smartphone, gas sensors.

Air pollution is a major contributor to the global disease burden. Scientific literature has been consistent for the past decades, exposing the dangers behind hazardous air quality in both indoor and outdoor environments, and its impact on human health and ecosystems. Conventional monitoring stations are expensive and bulky, limiting the spatiotemporal resolution of the provided data. For this reason, administrations are interested in adding low-cost sensors to the monitoring network, and thus obtaining measures from multiple sources to enhance the network’s density and coverage. In this work, we present a portable Instrumental Odour Monitoring System (IOMS) consisting of an electronic device with an array of metal oxide semiconductor (MOS) gas and Particle Matter sensors connected through Bluetooth with a smartphone with our own developed app that gathers data and sends it to a cloud. We tested the device in two different experimental setups, the first with laboratory-controlled conditions and the second in field, creating a network with the help of volunteers in the city of Barcelona.

Summary Desulfovibrio desulfuricans reduces Pd(II) to Pd(0)‐nanoparticles (Pd‐NPs) which are catalytically active in 2‐pentyne hydrogenation. To make Pd‐NPs, resting cells are challenged with Pd(II) ions (uptake), followed by addition of electron donor to promote bioreduction of cell‐bound Pd(II) to Pd(0) (bio‐Pd). Application of radiofrequency (RF) radiation to prepared 5 wt% bio‐Pd catalyst (60 W power, 60 min) increased the hydrogenation rate by 70% with no adverse impact on selectivity to cis‐2‐pentene. Such treatment of a 5 wt% Pd/carbon commercial catalyst did not affect the conversion rate but reduced the selectivity. Lower‐dose RF radiation (2–8 W power, 20 min) was applied to the bacteria at various stages before and during synthesis of the bio‐scaffolded Pd‐NPs. The reaction rate (μ mol 2‐pentyne converted s‐1) was increased by ~threefold by treatment during bacterial catalyst synthesis. Application of RF radiation (2 or 4 W power) to resting cells prior to Pd(II) exposure affected the catalyst made subsequently, increasing the reaction rate by 50% as compared to untreated cells, while nearly doubling selectivity for cis 2‐pentene. The results are discussed with respect to published and related work which shows altered dispersion of the Pd‐NPs made following or during RF exposure.

Three new sea fan species of Leptogorgia were discovered during multiple scuba diving expeditions along the Gulf of California coast and islands. Leptogorgia iridis sp. nov. is distributed in the southern region of the gulf (Mexican Province), inhabiting tropical rocky reefs of the Islas Marías Archipelago (Nayarit) and Bahía Banderas (Jalisco). This species has small colonies (< 7 cm height) with at least five clearly distinct chromotypes. Leptogorgia martirensis sp. nov. was found exclusively on the rocky reefs of San Pedro Mártir and San Esteban Islands located in the northern region of the Gulf of California (northern region of Cortez Province). Leptogorgia enrici sp. nov. is distributed from the south to the northern region of the Gulf of California (Cortez Province), inhabiting substrates of rocky reefs, sandy and pebbly sea floors. Comprehensive ecological diving expeditions to identify and classify octocorals in the Mexican Pacific (1995–2019) indicate that L. iridis sp. nov. and L. martirensis sp. nov. are likely to be micro-endemics and L. enrici sp. nov. is endemic to the Gulf of California, which defines their currently known biogeographic distribution ranges.

Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.

Concrete properties, including its compressive strength, are in general highly nonlinear functions of its components. Concrete mix design methods are basically simulations that require costly and time consuming adjustments in laboratory. A useful support tool based on artificial neural networks, using a multilayer perceptron network, is proposed in this paper as a means to predict compressive strength of concrete mixes. The developed models are useful for reducing the quantity of laboratory tests required for concrete mix design adjustments.

Hydrogen, an emergent alternative energy vector to fossil fuels, can be produced sustainably by fermentation of cellulose following hydrolysis. Fermentation feedstock was produced hydrolytically using hot compressed water. The addition of CO2 enhanced hydrolysis by ~26% between 240 and 260 °C with comparable hydrolysis products as obtained under N2 but at a 10 °C lower temperature. Co-production of inhibitory 5-hydromethyl furfural was mitigated via activated carbon sorption, facilitating fermentative biohydrogen production from the hydrolysate by Escherichia coli. Post-fermentation E. coli cells were recycled to biomanufacture supported Pd/Ru nanocatalyst to up-convert liquid-extracted 5-HMF to 2,5-dimethyl furan, a precursor of ‘drop in’ liquid fuel, in a one-pot reaction. This side stream up-valorisation mitigates against the high ‘parasitic’ energy demand of cellulose bioenergy, potentially increasing process viability via the coupled generation of two biofuels. This is discussed with respect to example data obtained via a hydrogen biotechnology with catalytic side stream up-conversion from cellulose feedstock.

Hydrothermal carbonization (HTC) is a thermochemical technology of biomass conversion that has some advantages: lower operating temperature than other technologies, ability to process biomass with high moisture content and generation of a final product with a higher calorific value than the original biomass and with hydrophobic characteristics. This study evaluated the influence of temperature, time, biomass blend, nature and dose of additive in the HTC process. The response variables were mass yield (MY) and higher heating value (HHV), generating a total of 128 experiments that were grouped in 8 complete factorial designs (24). Pressed olive (OLV), oat husks (AV), Pinus radiata sawdust (AS), and raps seeds (RPS) were used as raw biomass. MY and HHV had an R2 above 0.90 using the response equations of Experimental Design. Results indicate that temperature was the main effect in both responses, since it produced a decrease of MY between 5 and 10 %, and an increase of HHV between 1.5 and 2.2 MJ / kg. Use of additives did not significantly improve the energy yield. On the other hand, addition of a ‘more reactive’ biomass with increasing temperature achieved an important improvement in the HHV value. It is a positive fact because the studied biomass presents strong seasonal and geographical availability.

Escherichia coli cells support the nucleation and growth of ruthenium and ruthenium-palladium nanoparticles (Bio-Ru and Bio-Pd/Ru NPs). We report a method for the synthesis of these monometallic and bimetallic NPs and their application in the catalytic upgrading of 5-hydroxymethyl furfural (5-HMF) to 2,5 dimethylfuran (DMF). Examination using high resolution transmission electron microscopy with energy dispersive X-ray microanalysis (EDX) and high angle annular dark field (HAADF) showed Ru NPs located mainly at the cell surface using Ru(III) alone but small intracellular Ru-NPs (size ∼1–2 nm) were visible only in cells that had been pre-“seeded” with Pd(0) (5 wt%) and loaded with equimolar Ru. Pd(0) NPs were distributed between the cytoplasm and cell surface. Cells bearing 5% Pd/5% Ru showed some co-localization of Pd and Ru but chance associations were not ruled out. Cells loaded to 5 wt% Pd/20 wt% Ru showed evidence of core-shell structures (Ru core, Pd shell). Examination of this cell surface material using X-ray photoelectron spectroscopy (XPS) showed Pd(0) and Pd(II) and Ru(IV) and Ru(III), with confirmation by analysis of bulk material using X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) analyses. Both Bio-Ru NPs and Bio-Pd/Ru NPs were active in the conversion of 5-HMF into 2,5-DMF but commercial Ru on carbon catalyst outperformed 5 wt% bio-Ru by fourfold. While 5 wt% Pd/20 wt% Ru achieved 20% yield of DMF the performance of the 5 wt% Pd/5 wt% Ru bio-catalyst was higher and comparable to the commercial 5 wt% Ru/C catalyst in a test reaction using commercial 5-HMF (>50% selectivity). 5-HMF was prepared by thermochemical hydrolysis of starch and cellulose with solvent extraction of 5-HMF into methyltetrahydrofuran (MTHF). Here, with MTHF as the reaction solvent the commercial Ru/C catalyst had little activity (100% conversion, negligible selectivity to DMF) whereas the 5 wt% Pd/5 wt% Ru bio-bimetallic gave 100% conversion and 14% selectivity to DMF from material extracted from hydrolyzates. The results indicate a potential green method for realizing increased energy potential from biomass wastes as well as showing a bio-based pathway to manufacturing a scarcely described bimetallic material.

Biogas-energy is marginally profitable against the “parasitic” energy demands of processing biomass. Biogas involves microbial fermentation of feedstock hydrolyzate generated enzymatically or thermochemically. The latter also produces 5-hydroxymethyl furfural (5-HMF) which can be catalytically upgraded to 2, 5-dimethyl furan (DMF), a “drop in fuel.” An integrated process is proposed with side-stream upgrading into DMF to mitigate the “parasitic” energy demand. 5-HMF was upgraded using bacterially-supported Pd/Ru catalysts. Purpose-growth of bacteria adds additional process costs; Pd/Ru catalysts biofabricated using the sulfate-reducing bacterium (SRB) Desulfovibrio desulfuricans were compared to those generated from a waste consortium of acidophilic sulfidogens (CAS). Methyl tetrahydrofuran (MTHF) was used as the extraction-reaction solvent to compare the use of bio-metallic Pd/Ru catalysts to upgrade 5-HMF to DMF from starch and cellulose hydrolyzates. MTHF extracted up to 65% of the 5-HMF, delivering solutions, respectively, containing 8.8 and 2.2 g 5-HMF/L MTHF. Commercial 5% (wt/wt) Ru-carbon catalyst upgraded 5-HMF from pure solution but it was ineffective against the hydrolyzates. Both types of bacterial catalyst (5wt%Pd/3-5wt% Ru) achieved this, bio-Pd/Ru on the CAS delivering the highest conversion yields. The yield of 5-HMF from starch-cellulose thermal treatment to 2,5 DMF was 224 and 127 g DMF/kg extracted 5-HMF, respectively, for CAS and D. desulfuricans catalysts, which would provide additional energy of 2.1 and 1.2 kWh/kg extracted 5-HMF. The CAS comprised a mixed population with three patterns of metallic nanoparticle (NP) deposition. Types I and II showed cell surface-localization of the Pd/Ru while type III localized NPs throughout the cell surface and cytoplasm. No metallic patterning in the NPs was shown via elemental mapping using energy dispersive X-ray microanalysis but co-localization with sulfur was observed. Analysis of the cell surfaces of the bulk populations by X-ray photoelectron spectroscopy confirmed the higher S content of the CAS bacteria as compared to D. desulfuricans and also the presence of Pd-S as well as Ru-S compounds and hence a mixed deposit of PdS, Pd(0), and Ru in the form of various +3, +4, and +6 oxidation states. The results are discussed in the context of recently-reported controlled palladium sulfide ensembles for an improved hydrogenation catalyst.

Composite spectrum (CS) is a data-fusion technique that reduces the number of spectra to be analyzed, simplifying the analysis process for machine monitoring and fault detection. In this work, vibration signals from five components of a combine harvester (thresher, chopper, straw walkers, sieve box, and engine) are obtained by placing four accelerometers along the combine-harvester chassis in non-optimal locations. Four individual spectra (one from each accelerometer) and three CS (non-coherent, coherent and poly-coherent spectra) from 18 cases are analyzed. The different cases result from the combination of three working conditions of the components—deactivated (off), balanced (healthy), and unbalanced (faulty)—and two speeds—idle and maximum revolutions per minute (RPM). The results showed that (i) the peaks can be identified in the four individual spectra that correspond to the rotational speeds of the five components in the analysis; (ii) the three formulations of the CS retain the relevant information from the individual spectra, thereby reducing the number of spectra required for monitoring and detecting rotating unbalances within a combine harvester; and, (iii) data noise reduction is observed in coherent and poly-coherent CS with respect to the non-coherent CS and the individual spectra. This study demonstrates that the rotating unbalances of various components within agricultural machines, can be detected with a reduced number of accelerometers located in non-optimal positions, and that it is feasible to simplify the monitoring with CS. Overall, the coherent CS may be the best composite spectra formulation in order to monitor and detect rotating unbalances in agricultural machines.

The environmental conditions for the planned geological disposal of radioactive waste —including hyper-alkaline pH, radiation or anoxia—are expected to be extremely harsh for microbial activity. However, it is thought that microbial communities will develop in these repositories, and this would have implications for geodisposal integrity and the control of radionuclide migration through the surrounding environment. Nuclear waste contains radioactive isotopes of selenium (Se) such as 79Se, which has been identified as one of the main radionuclides in a geodisposal system. Here, we use the bacterial species Stenotrophomonas bentonitica, isolated from bentonites serving as an artificial barrier reference material in repositories, to study the reduction of selenite (SeIV) under simulated geodisposal conditions. This bacterium is able to reduce toxic SeIV anaerobically from a neutral to alkaline initial pH (up to pH 10), thereby producing elemental selenium (Se0) nanospheres and nanowires. A transformation process from amorphous Se (a-Se) nanospheres to trigonal Se (t-Se) nanowires, through the formation of monoclinic Se (m-Se) aggregates as an intermediate step, is proposed. The lesser solubility of Se0 and t-Se makes S. bentonitica a potential candidate to positively influence the security of a geodisposal system, most probably with lower efficiency rates than those obtained aerobically.

This study is aimed at (i) estimating the angular leaf spot (ALS) disease severity in common beans crops in Brazil, caused by the fungus Pseudocercospora griseola, employing leaf and canopy spectral reflectance data, (ii) evaluating the informative spectral regions in the detection, and (iii) comparing the estimation accuracy when the reflectance or the first derivative reflectance (FDR) is employed. Three data sets of useful spectral reflectance measurements in the 440 to 850 nm range were employed; measurements were taken over the leaves and canopy of bean crops with different levels of disease. A system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra. Levels of disease to be taken as true reference were determined from the proportion of the total leaf surface covered by necrotic lesions on RGB images. When estimating ALS disease severity in bean crops by using hyperspectral reflectance spectrometry, this study suggests that (i) successful estimations with coefficients of determination up to 0.87 can be achieved if the spectra is acquired by the spectroradiometer in contact with the leaves, (ii) unsuccessful estimations are obtained when the spectra are acquired by the spectroradiometer from one or more meters above the crop, (iii) the red to near-infrared spectral region (630-850 nm) offers the same precision in the estimation as the blue to near-infrared spectral region (440-850), and (iv) neither significant improvements nor significant detriments are achieved when the input data to the estimation processing system are the FDR spectra, instead of the reflectance spectra.

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

Farmers experience whole-body vibrations when they drive tractors. Among the various factors that influence the vibrations to which the driver is exposed are terrain roughness, tractor speed, tire type and pressure, rear axle width, and tractor seat height above the ground. In this paper the influence of tractor seat height above the ground on the lateral vibrations to which the tractor driver is exposed is studied by means of a geometrical and an experimental analysis. Both analyses show that: (i) lateral vibrations experienced by a tractor driver increase linearly with tractor-seat height above the ground; (ii) lateral vibrations to which the tractor driver is exposed can equal or exceed vertical vibrations; (iii) in medium-size tractors, a feasible 30 cm reduction in the height of the tractor seat, which represents only 15% of its current height, will reduce the lateral vibrations by around 20%; and (iv) vertical vibrations are scarcely influenced by tractor-seat height above the ground. The results suggest that manufacturers could increase the comfort of tractors by lowering tractor-seat height above the ground, which will reduce lateral vibrations.

ABSTRACT Streptolysin S (SLS)-like virulence factors from clinically relevant Gram-positive pathogens have been proposed to behave as potent cytotoxins, playing key roles in tissue infection. Listeriolysin S (LLS) is an SLS-like hemolysin/bacteriocin present among Listeria monocytogenes strains responsible for human listeriosis outbreaks. As LLS cytotoxic activity has been associated with virulence, we investigated the LLS-specific contribution to host tissue infection. Surprisingly, we first show that LLS causes only weak red blood cell (RBC) hemolysis in vitro and neither confers resistance to phagocytic killing nor favors survival of L. monocytogenes within the blood cells or in the extracellular space (in the plasma). We reveal that LLS does not elicit specific immune responses, is not cytotoxic for eukaryotic cells, and does not impact cell infection by L. monocytogenes. Using in vitro cell infection systems and a murine intravenous infection model, we actually demonstrate that LLS expression is undetectable during infection of cells and murine inner organs. Importantly, upon intravenous animal inoculation, L. monocytogenes is found in the gastrointestinal system, and only in this environment LLS expression is detected in vivo. Finally, we confirm that LLS production is associated with destruction of target bacteria. Our results demonstrate therefore that LLS does not contribute to L. monocytogenes tissue injury and virulence in inner host organs as previously reported. Moreover, we describe that LlsB, a putative posttranslational modification enzyme encoded in the LLS operon, is necessary for murine inner organ colonization. Overall, we demonstrate that LLS is the first SLS-like virulence factor targeting exclusively prokaryotic cells during in vivo infections. IMPORTANCE The most severe human listeriosis outbreaks are caused by L. monocytogenes strains harboring listeriolysin S (LLS), previously described as a cytotoxin that plays a critical role in host inner tissue infection. Cytotoxic activities have been proposed as a general mode of action for streptolysin S (SLS)-like toxins, including clostridiolysin S and LLS. We now challenge this dogma by demonstrating that LLS does not contribute to virulence in vivo once the intestinal barrier has been crossed. Importantly, we show that intravenous L. monocytogenes inoculation leads to bacterial translocation to the gastrointestinal system, where LLS is specifically expressed, targeting the host gut microbiota. Our study highlights the heterogeneous modes of action of SLS-like toxins, and we demonstrate for the first time a further level of complexity for SLS-like biosynthetic clusters as we reveal that the putative posttranslational modification enzyme LlsB is actually required for inner organ colonization, independently of the LLS activity.

Introducción: el tracto gastrointestinal es un sitio frecuentemente afectado por el VIH; sin embargo, en la práctica clínica algunos hallazgos normales en la endoscopia pueden no serlo, lo cual conlleva a estos pacientes a no recibir tratamiento oportuno para patologías gastrointestinales. Métodos: estudio observacional descriptivo en pacientes con VIH que consultaron por síntomas digestivos y que requirieron endoscopia y/o colonoscopia durante el año 2014 en el Hospital Universitario de Santander. Se tomaron datos sociodemográficos, clínicos y paraclínicos. Se realizaron 41 endoscopias altas y 29 colonoscopias en 54 pacientes. A todo estudio se le practicó biopsia. Resultados: la edad promedio fue de 39 años, la sintomatología digestiva con mayor reporte fue la diarrea y las lesiones orales; 87% tenían conteo menor a 200 CD4, solo 24% reciben actualmente TARAE. Los diagnósticos más frecuentes por anatomía patológica diferentes a normalidad fueron: Candidiasis (17%) en esófago, gastritis crónica moderada (26,7%) en estómago y colitis crónica inespecífica moderada tanto en colon izquierdo (44,8%) como en colon derecho (51,7%). Conclusiones: los hallazgos endoscópicos y patológicos concuerdan con la frecuencia de presentaciones reportadas en la literatura, aunque no se encontraron neoplasias ni agentes infecciosos como micobacterias. Los oportunistas más frecuentes fueron la cándida y el citomegalovirus. Al confrontar el diagnóstico de normalidad entre el endoscopista y la anatomía patológica, solamente en esófago había una concordancia aceptable, a diferencia del estómago y el colon, donde la disparidad es evidente. Por lo anterior, en pacientes con VIH/ SIDA que requieran endoscopia sería importante siempre considerar la toma biopsias de manera protocolizada

In this paper, we investigate the Koszul behavior of finitely semi-graded algebras by the distributivity of some associated lattice of ideals. The Hilbert series, the Poincaré series, and the Yoneda algebra are defined for this class of algebras. Moreover, the point modules and the point functor are introduced for finitely semi-graded rings. Finitely semi-graded algebras and rings include many important examples of non- N -graded algebras coming from mathematical physics that play a very important role in mirror symmetry problems, and for these concrete examples, the Koszulity will be established, as well as the explicit computation of its Hilbert and Poincaré series.

Numerous studies have focused on the bacterial synthesis of palladium nanoparticles (bio-Pd NPs), via uptake of Pd (II) ions and their enzymatically-mediated reduction to Pd (0). Cells of Desulfovibrio desulfuricans (obligate anaerobe) and Escherichia coli (facultative anaerobe, grown anaerobically) were exposed to low-dose radiofrequency (RF) radiation(microwave (MW) energy) and the biosynthesized Pd NPs were compared. Resting cells were exposed to microwave energy before Pd (II)-challenge. MW-injured Pd (II)-treated cells (and non MW-treated controls) were contacted with H2 to promote Pd(II) reduction. By using scanning transmission electron microscopy (STEM) associated with a high-angle annular dark field (HAADF) detector and energy dispersive X-ray (EDX) spectrometry, the respective Pd NPs were compared with respect to their mean sizes, size distribution, location, composition, and structure. Differences were observed following MWinjury prior to Pd(II) exposure versus uninjured controls. With D. desulfuricans the bio-Pd NPs formed post-injury showed two NP populations with different sizes and morphologies. The first, mainly periplasmically-located, showed polycrystalline Pd nano-branches with different crystal orientations and sizes ranging between 20 and 30 nm. The second NPpopulation, mainly located intracellularly, comprised single crystals with sizes between 1 and 5 nm. Bio-Pd NPs were produced mainly intracellularly by injured cells of E. coli and comprised single crystals with a size distribution between 1 and 3 nm. The polydispersity index was reduced in the bio-Pd made by injured cells of E. coli and D. desulfuricans to 32% and 39%, respectively, of the values of uninjured controls, indicating an increase in NP homogeneity of 30−40% as a result of the prior MWinjury. The observations are discussed with respect to the different locations of Pd(II)-reducing hydrogenases in the two organisms and with respect to potential implications for the catalytic activity of the produced NPs following injury-associated altered NP patterning.

Low-cost GPS receivers provide geodetic positioning information using the NMEA protocol, usually with eight digits for latitude and nine digits for longitude. When these geodetic coordinates are converted into Cartesian coordinates, the positions fit in a quantization grid of some decimeters in size, the dimensions of which vary depending on the point of the terrestrial surface. The aim of this study is to reduce the quantization errors of some low-cost GPS receivers by using a Kalman filter. Kinematic tractor model equations were employed to particularize the filter, which was tuned by applying Monte Carlo techniques to eighteen straight trajectories, to select the covariance matrices that produced the lowest Root Mean Square Error in these trajectories. Filter performance was tested by using straight tractor paths, which were either simulated or real trajectories acquired by a GPS receiver. The results show that the filter can reduce the quantization error in distance by around 43%. Moreover, it reduces the standard deviation of the heading by 75%. Data suggest that the proposed filter can satisfactorily preprocess the low-cost GPS receiver data when used in an assistance guidance GPS system for tractors. It could also be useful to smooth tractor GPS trajectories that are sharpened when the tractor moves over rough terrain.

This paper presents an Artificial Neural Network (ANN) based model for environmental variables related to the tobacco drying process. A fitting ANN was used to estimate and predict temperature and relative humidity inside the tobacco dryer: the estimation consists of calculating the value of these variables in different locations of the dryer and the prediction consists of forecasting the value of these variables with different time horizons. The proposed model has been validated with temperature and relative humidity data obtained from a real tobacco dryer using a Wireless Sensor Network (WSN). On the one hand, an error under 2% was achieved, obtaining temperature as a function of temperature and relative humidity in other locations in the estimation task. Besides, an error around 1.5 times lower than the one obtained with an interpolation method was achieved in the prediction task when the temperature inside the tobacco mass was predicted with time horizons over 2.5 hours as a function of its present and past values. These results show that ANN-based models can be used to improve the tobacco drying process because with these types of models the value of environmental variables can be predicted in the near future and can be estimated in other locations with low errors.

This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.

Background: Low-grade chronic inflammation is a common feature of obesity and its cardio-metabolic complications. However, little is known about a possible causal role of inflammation in metabolic disorders. Mexico is among the countries with the highest obesity rates in the world and the admixed Mexican population is a relevant sample due to high levels of genetic diversity. Methods: Here, we studied 1,462 Mexican children recruited from Mexico City. Six genetic variants in five inflammation-related genes were genotyped: rs1137101 (leptin receptor (LEPR)), rs7305618 (hepatocyte nuclear factor 1 alpha (HNF1A)), rs1800629 (tumor necrosis factor alpha (TNFA)), rs1800896, rs1800871 (interleukin-10 (IL-10)), rs1862513 (resistin (RETN)). Ten continuous and eight binary traits were assessed. Linear and logistic regression models were used adjusting for age, sex, and recruitment centre. Results: We found that one SNP displayed a nominal evidence of association with a continuous trait: rs1800871 (IL-10) with LDL (beta = −0.068 ± 1.006, P = 0.01). Subsequently, we found one nominal association with a binary trait: rs7305618 (HNF1A) with family history of hypertension (odds-ratio = 1.389 [1.054–1.829], P = 0.02). However, no P-value passed the Bonferroni correction for multiple testing. Discussion: Our data in a Mexican children population are consistent with previous reports in European adults in failing to demonstrate an association between inflammation-associated single nucleotide polymorphisms (SNPs) and metabolic traits.

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering.

Error is always present in the GPS guidance of a tractor along a desired trajectory. One way to reduce GPS guidance error is by improving the tractor positioning. The most commonly used ways to do this are either by employing more precise GPS receivers and differential corrections or by employing GPS together with some other local positioning systems such as electronic compasses or Inertial Navigation Systems (INS). However, both are complex and expensive solutions. In contrast, this article presents a simple and low cost method to improve tractor positioning when only a GPS receiver is used as the positioning sensor. The method is based on placing the GPS receiver ahead of the tractor, and on applying kinematic laws of tractor movement, or a geometric approximation, to obtain the midpoint position and orientation of the tractor rear axle more precisely. This precision improvement is produced by the fusion of the GPS data with tractor kinematic control laws. Our results reveal that the proposed method effectively reduces the guidance GPS error along a straight trajectory.

This article proposes a Radial Basis Function Artificial Neural Network (RBF-ANN) to classify tempered steel cams as correctly or incorrectly treated pieces by using multi-frequency nondestructive eddy current testing. Impedances at five frequencies between 10 kHz and 300 kHz were employed to perform the binary sorting. The ANalysis Of VAriance (ANOVA) test was employed to check the significance of the differences between the impedance samples for the two classification groups. Afterwards, eleven classifiers were implemented and compared with one RBF-ANN classifier: ten linear discriminant analysis classifiers and one Euclidean distance classifier. When employing the proposed RBF-ANN, the best performance was achieved with a precision of 95% and an area under the Receiver Operating Characteristic (ROC) curve of 0.98. The obtained results suggest RBF-ANN classifiers processing multi-frequency impedance data could be employed to classify tempered steel DIN 100Cr6 cams with a better performance than other classical classifiers.

Many motor applications require accurate speed measurement. For brushed dc motors, speed can be measured with conventional observers or sensorless observers. Sensorless observers have the advantage of not requiring any external devices to be attached to the motor. Instead, voltage and/or current are measured and used to estimate the speed. The sensorless observers are usually divided into two groups: those based on the dynamic model, and those based on the ripple component. This paper proposes a method that measures the current of brushed dc motors and analyses the position of its spectral components. From these spectral components, the method estimates the motor speed. Three tests, performed each with the speeds ranging from 2000 to 3000 rpm either at constant-speed, at slowly changing speeds, or at rapidly changing speeds, showed that the average error was below 1 rpm and that the deviation error was below 1.5 rpm. The proposed method: (i) is a novel method that is not based on either the dynamic model or on the ripple component; (ii) requires only the measurement of the current for the speed estimation; (iii) can be used for brushed dc (direct current) motors with a large number of coils; and (iv) achieves a low error in the speed estimation.