Short Sampling Time Research Articles

Regarding Bioanalysis Lasting a Few Minutes: Automated Cooling-SPME and Fast-GC for Urinary 2-Phenyl-2-Propanol Monitoring.

An innovative SPME head space GC-MS method, in cooling mode, using a fully automated routine, was developed to detect 2-phenyl-2-propanol, a representative urinary metabolite of cumene. Following an acid hydrolysis and derivatization step with lowered quantities of reagents, acetic anhydride and pyridine, a 30 μm polydimethylsiloxane SPME fiber was used to sample derivatized 2-phenyl-2-propanol, such as benzenemethanol,α,α-dimethyl-acetate, from the headspace. Performances of the method, optimized through experimental design, provide an LOD of 0.034 mg/L and an LOQ 0.10 mg/L, with a short sampling time necessary per sample. The method, developed on standard solutions, will be applied to both occupationally exposed and non-exposed populations.

A study method using early dynamic acquisition of [18F]fluorodopa positron emission tomography for the differential diagnosis between progression and radionecrosis of brain metastases after radiotherapy

BackgroundIt is difficult to distinguish between the brain metastasis progression (BMP) and brain radionecrosis (BRN) on the basis of 18F-3,4-dihydroxyphenylalanine positron emission tomography/computed-tomography (18F-FDOPA PET/CT) data. The advent of silicon photomultiplier (SiPM) PET technology makes it possible to study dynamic volumes and potentially improve diagnostic accuracy. We developed a method for processing 18F-FDOPA PET/CT in the differential diagnosis between BMP and BRN. The method involves a short (3-second) sampling time during a 4-minute acquisition on a SiPM-PET/CT machine. We prospectively included 15 patients and 19 metastases. All acquisitions were performed in list mode acquisition for 25 min on a four-ring SiPM PET/CT system. We calculated the ratios between the maximum activity in the lesion’s voxel and the mean activity in the contralateral region (VOImax/CLmean) or the mean activity in the white matter (VOImax/WMmean).ResultsSeven lesions were classified as BMP and twelve were classified as BRN. Statistically significant intergroup differences in the VOImax/CLmean and VOImax/WMmean activity ratios were observed for both the clinical volume and the early acquisition. The best performing quantitative variable was the VOImax/CLmean ratio on early acquisition, with a diagnostic accuracy of 94.7%, a sensitivity of 100%, and a specificity of 91.7%.ConclusionThe 18F-FDOPA PET/CT data acquired a few minutes after the bolus injection confirms its value in differentiating between BMP and BRN, compared to the much longer classic clinical protocol.

Low-Cost Electronic Nose for Robusta and Arabica Coffee Classification Using 1-D Convolutional Neural Networks

Abstract The use of an electronic nose (e-nose) employing MQ-series sensors has become increasingly popular due to its cost-effectiveness. However, the impact of varying sampling durations on its performance, particularly in distinguishing between Robusta and Arabica coffee, has not been extensively studied. This research investigates how different sampling durations affect the e-nose’s classification accuracy. The study utilizes a 1D Convolutional Neural Network (1D-CNN) and a Support Vector Machine (SVM), with both models enhanced by a Savitzky-Golay filter to reduce noise and improve data quality. Feature selection techniques are applied to address data complexity and improve model performance. The experimental results indicate that the 1D-CNN model achieves optimal accuracy of up to 100% at a sampling duration of 200 seconds, while the SVM reaches approximately 92% accuracy under the same conditions. Notably, for applications requiring faster processing times, the SVM proves to be more effective, achieving 70% accuracy at a 20-second sampling interval, compared to 60% for the 1D-CNN. The study demonstrates that integrating MQ sensors in e-nose systems can yield effective classification results for coffee varieties, even with shorter sampling times. These findings have the potential to influence the development of cost-effective e-nose devices, making them more accessible to small and medium-sized industrial enterprises.

Performance of a Novel Electronic Nose for the Detection of Volatile Organic Compounds Relating to Starvation or Human Decomposition Post-Mass Disaster.

There has been a recent increase in the frequency of mass disaster events. Following these events, the rapid location of victims is paramount. Currently, the most reliable search method is scent detection dogs, which use their sense of smell to locate victims accurately and efficiently. Despite their efficacy, they have limited working times, can give false positive responses, and involve high costs. Therefore, alternative methods for detecting volatile compounds are needed, such as using electronic noses (e-noses). An e-nose named the 'NOS.E' was developed and has been used successfully to detect VOCs released from human remains in an open-air environment. However, the system's full capabilities are currently unknown, and therefore, this work aimed to evaluate the NOS.E to determine the efficacy of detection and expected sensor response. This was achieved using analytical standards representative of known human ante-mortem and decomposition VOCs. Standards were air diluted in Tedlar gas sampling bags and sampled using the NOS.E. This study concluded that the e-nose could detect and differentiate a range of VOCs prevalent in ante-mortem and decomposition VOC profiles, with an average LOD of 7.9 ppm, across a range of different chemical classes. The NOS.E was then utilized in a simulated mass disaster scenario using donated human cadavers, where the system showed a significant difference between the known human donor and control samples from day 3 post-mortem. Overall, the NOS.E was advantageous: the system had low detection limits while offering portability, shorter sampling times, and lower costs than dogs and benchtop analytical instruments.

LAMP combined with Pyrococcus furiosus Argonaute for the ultrasensitive and highly specific point-of-care test platform for Listeria monocytogenes detection

Listeria monocytogenes is an important foodborne pathogen that causes serious infectious diseases in humans and animals. Rapid, accurate, and sensitive detection in the field is critical for the timely implementation of control measures and ensuring food safety. The study established a method that combines loop-mediated isothermal amplification (LAMP) technology and Pyrococcus furiosus Argonaute (PfAgo) reporter system, which can detect L. monocytogenes hly gene levels as low as 1.8 × 101 copies. To overcome the demands on instrumentation and equipment, lateral flow strip (LFS) technology was combined for the first time in a LAMP-PfAgo-based system to detect L. monocytogenes. The method was highly sensitive, with a limit of detection of 1.8 × 102copies, and specificity results showed no cross-reactivity with other pathogens. The performance of the method was comparable to that of real-time fluorescence quantitative PCR (qPCR) in the detection of 18 food origin samples. Moreover, the LAMP-PfAgo reaction was equipped a low-cost thin metal bath as a simple amplification device, allowing it to detect L. monocytogenes samples in shorter time, highlighting its potential as a portable, rapid, accurate, and visual nucleic acid assay. The method can facilitate the early detection of pathogenic infections and promoting food safety detection technologies.

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

Vessel trajectory data are currently the most important data source for vessel trajectory data mining research. However, vessel AIS data have a short sampling time interval and a large amount of data redundancy, which hampers the efficient utilization of AIS data. In order to effectively remove redundant information from AIS data and improve its usage efficiency, a compression algorithm for vessel trajectory data compression algorithm considering critical region identification (VATDC_CCRI) is proposed. The VATDC_CCRI algorithm identifies the critical regions of a vessel’s trajectory by analyzing the distribution of node variation rates. It employs the Douglas–Peucker (DP) algorithm to compress the data in these critical regions, reducing the distortion of the trajectory after compression. Additionally, the algorithm utilizes a sliding window approach to process the initial trajectory to improve the quality of the compressed vessel trajectories and retain as many spatiotemporal characteristics of the original trajectories as possible. It combines the feature nodes from the crucial regions in the vessel’s trajectory with the results obtained from the sliding window algorithm, effectively compressing the vessel’s trajectory. Experiments conducted on individual and multiple trajectories demonstrate that the VATDC_CCRI algorithm achieves higher compression rates and exhibits faster processing speeds compared to other classical vessel trajectory compression algorithms while preserving the shape of the vessel’s trajectory significantly.

Unraveling the secrets of plant roots: Simplified method for large scale root exudate sampling and analysis in Arabidopsis thaliana

Background Plants exude a plethora of compounds to communicate with their environment. Although much is known about above-ground plant communication, we are only beginning to fathom the complexities of below-ground chemical communication channels. Studying root-exuded compounds and their role in plant communication has been difficult due to the lack of standardized methodologies. Here, we develop an interdisciplinary workflow to explore the natural variation in root exudate chemical composition of the model plant Arabidopsis thaliana. We highlight key challenges associated with sampling strategies and develop a framework for analyzing both narrow- and broad-scale patterns of root exudate composition in a large set of natural A. thaliana accessions. Methods Our method involves cultivating individual seedlings in vitro inside a plastic mesh, followed by a short hydroponic sampling period in small quantities of ultrapure water. The mesh makes it easy to handle plants of different sizes and allows for large-scale characterization of individual plant root exudates under axenic conditions. This setup can also be easily extended for prolonged temporal exudate collection experiments. Furthermore, the short sampling time minimizes the duration of the experiment while still providing sufficient signal even with small volume of the sampling solution. We used ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) for untargeted metabolic profiling, followed by tentative compound identification using MZmine3 and SIRIUS 5 software, to capture a broad overview of root exudate composition in A. thaliana accessions. Results Based on 28 replicates of the Columbia genotype (Col-0) compared with 10 random controls, MZmine3 annotated 354 metabolites to be present only in Col-0 by negative ionization. Of these, 254 compounds could be annotated by SIRIUS 5 software. Conclusions The methodology developed in this study can be used to broadly investigate the role of root exudates as chemical signals in plant belowground interactions.

Use of silicone wristbands to measure firefighters’ exposures to polycyclic aromatic hydrocarbons (PAHs) during live fire training

Firefighters experience exposures to carcinogenic and mutagenic substances, including polycyclic aromatic hydrocarbons (PAHs). Silicone wristbands (SWBs) have been used as passive samplers to assess firefighters' exposures over the course of a shift but their utility in measuring short term exposures, source of exposure, and correlations with other measurements of exposure have not yet been investigated. In this study, SWBs were used to measure the concentrations of 16 priority PAHs inside and outside of firefighters’ personal protective equipment (PPE) while firefighting. SWBs were placed on the wrist and jacket of 20 firefighters conducting live fire training. Correlations were made with matching data from a sister project that measured urinary concentrations of PAH metabolites and PAH concentrations from personal air samples from the same participants. Naphthalene, acenaphthylene and phenanthrene had the highest geometric mean concentrations in both jacket and wrist SWB, with 1040, 320, 180 ng/g SWB for jacket and 55.0, 4.9, and 6.0 ng/g SWB for wrist, respectively. Ratios of concentrations between the jacket and wrist SWBs were calculated as worker protection factors (WPFs) and averaged 40.1 for total PAHs and ranged from 2.8 to 214 for individual PAHs, similar to previous studies. Several significant correlations were observed between PAHs in jacket SWBs and air samples (e.g., total and low molecular weight PAHs, r = 0.55 and 0.59, p < 0.05, respectively). A few correlations were found between PAHs from SWBs worn on the wrist and jacket, and urinary concentrations of PAH metabolites and PAH concentrations in air samples. The ability of the SWBs to accurately capture exposures to various PAHs was likely influenced by short sampling time, high temperatures, and high turbulence. Future work should further examine the limitations of SWBs for PAH exposures in firefighting, and other extreme environments.

Comparison of the safety, effectiveness, and usability of swab robot vs. manual nasopharyngeal specimen collection

BackgroundHealthcare workers face a risk of infection during aerosol-generating procedures, such as nasal swabbing. Robot-assisted nasopharyngeal sampling aims to minimize this risk and reduce stress for healthcare providers. However, its effectiveness and safety require validation. MethodsWe conducted a controlled trial with 80 subjects at two teaching hospitals and compared robot-collected vs manually-collected nasopharyngeal swabs. The primary outcomes included specimen quality and success rate of nasopharyngeal swab collection. We also recorded the pain index, duration of the collection, and psychological stress using a post-collection questionnaire. ResultsDuring the study period, from September 23 to October 27, 2020, 40 subjects were enrolled in both the robotic and manual groups. The cycle threshold (Ct) value for nasopharyngeal specimens was statistically higher in the robotic group compared to the manual group (30.9 vs 28.0, p < 0.01). Both groups had Ct values under 35, indicating good quality specimens. In the robotic group, 3 out of 40 subjects required a second attempt at specimen collection, resulting in a success rate of 92.5 %. Further, although the pain levels were lower in the robotic group, the difference was not statistically significant (2.8 vs 3.6, p = 0.07). The manual group had a shorter sampling time, which was 29 s (201 vs 29, p < 0.05). However, when factoring in the time needed to put on personal protective equipment, the average time for the manual group increased to 251 s (201 vs 251, p < 0.05). Participants' questionnaire results show comparable psychological stress in both groups. Medical staff expressed that using a robot would reduce their psychological stress. ConclusionsWe propose a safe and effective robotic technology for collecting nasopharyngeal specimens without face-to-face contact, which may reduce the stress of physicians and nurses. This technology can also be optimized for efficiency, making it useful in situations where droplet-transmitted infectious diseases are a concern.

Unraveling the secrets of plant roots: Simplified method for large scale root exudate sampling and analysis in Arabidopsis thaliana.

Background: Plants exude a plethora of compounds to communicate with their environment. Although much is known about above-ground plant communication, we are only beginning to fathom the complexities of below-ground chemical communication channels. Studying root-exuded compounds and their role in plant communication has been difficult due to the lack of standardized methodologies. Here, we develop an interdisciplinary workflow to explore the natural variation in root exudate chemical composition of the model plant Arabidopsis thaliana. We highlight key challenges associated with sampling strategies and develop a framework for analyzing both narrow- and broad-scale patterns of root exudate composition in a large set of natural A. thaliana accessions. Methods: Our method involves cultivating individual seedlings in vitro inside a plastic mesh, followed by a short hydroponic sampling period in small quantities of ultrapure water. The mesh makes it easy to handle plants of different sizes and allows for large-scale characterization of individual plant root exudates under axenic conditions. This setup can also be easily extended for prolonged temporal exudate collection experiments. Furthermore, the short sampling time minimizes the duration of the experiment while still providing sufficient signal even with small volume of the sampling solution. We used ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) for untargeted metabolic profiling, followed by tentative compound identification using MZmine3 and SIRIUS 5 software, to capture a broad overview of root exudate composition in A. thaliana accessions. Results: Based on 28 replicates of the Columbia genotype (Col-0) compared with 10 random controls, MZmine3 identified 354 metabolites to be present only in Col-0 by negative ionization. Of these, 254 compounds could be annotated by SIRIUS 5 software. Conclusions: The methodology developed in this study can be used to broadly investigate the role of root exudates as chemical signals in plant belowground interactions.

Laplace and State-Space Methods for Calculating the Heat Losses in Case of Heavyweight Building Elements and Short Sampling Times

Reducing heat losses through the building envelope is one of the most important aspects to be met if the targets set by the European Union are to be achieved. In order to obtain a more realistic energy demand, dynamic heat transfer simulations are used to calculate the energy consumption of buildings, since steady-state calculations do not take into account the thermal mass in buildings. These dynamic simulations employ methods based on analytical models since numerical models are unsuitable for longer time periods. The analytical models used herein fall into the category of conduction transfer functions (CTFs). Two methods for computing CTFs that are addressed in this research are the Laplace method and the State-Space method. The objective of this paper is to verify the efficiency of the Laplace and State-Space methods for calculating the energy demand of a building in the case of heavyweight building elements and shorter sampling times, and to provide a means for improving the algorithms used by these methods. The Laplace and State-Space method algorithms were implemented in Mathematica, and the results were compared to EnergyPlus and TRNSYS, which use similar algorithms to calculate energy demand. It was shown in this paper that for the heavyweight wall element and a time step of 0.25 h, the difference between the total energy transferred through the inner surface was about 31% for EnergyPlus and 78% for TRNSYS compared to the reference solution. For the lightweight wall element, the results were stable for the time step of 0.25 h, but for the time step of 0.1 h, the differences were 45.64% and 303% between EnergyPlus, TRNSYS and the reference solution, respectively, compared to the State-Space and Laplace methods for which the maximum difference was 12.03% with a time step of 0.1 h. While dynamic heat transfer simulations are better than calculations based on steady-state boundary conditions, they also have their limitations and could lead to unsatisfactory results for short sampling times and if not applied properly.

Surface guided electron FLASH radiotherapy for canine cancer patients.

During recent years FLASH radiotherapy (FLASH-RT) has shown promising results in radiation oncology, with the potential to spare normal tissue while maintaining the antitumor effects. The high speed of the FLASH-RT delivery increases the need for fast and precise motion monitoring to avoid underdosing the target. Surface guided radiotherapy (SGRT) uses surface imaging (SI) to render a 3D surface of the patient. SI provides real-time motion monitoring and has a large scanning field of view, covering off-isocentric positions. However, SI has so far only been used for human patients with conventional setup and treatment. The aim of this study was to investigate the performance of SI as a motion management tool during electron FLASH-RT of canine cancer patients. To evaluate the SI system's ability to render surfaces of fur, three fur-like blankets in white, grey, and black were used to imitate the surface of canine patients and the camera settings were optimized for each blanket. Phantom measurements using the fur blankets were carried out, simulating respiratory motion and sudden shift. Respiratory motion was simulated using the QUASAR Respiratory Motion Phantom with the fur blankets placed on the phantom platform, which moved 10mm vertically with a simulated respiratory period of 4 s. Sudden motion was simulated with an in-house developed phantom, consisting of a platform which was moved vertically in a stepwise motion at a chosen frequency. For sudden measurements, 1, 2, 3, 4, 5, 6, 7, and 10Hz were measured. All measurements were both carried out at the conventional source-to-surface distance (SSD) of 100cm, and in the locally used FLASH-RT setup at SSD=70cm. The capability of the SI system to reproduce the simulated motion and the sampling time were evaluated. As an initial step towards clinical implementation, the feasibility of SI for surface guided FLASH-RT was evaluated for 11 canine cancer patients. The SI camera was capable of rendering surfaces for all blankets. The deviation between simulated and measured mean peak-to-peak breathing amplitude was within 0.6mm for all blankets. The sampling time was generally higher for the black fur than for the white and grey fur, for the measurement of both respiratory and sudden motion. The SI system could measure sudden motion within 62.5ms and detect motion with a frequency of 10Hz. The feasibility study of the canine patients showed that the SI system could be an important tool to ensure patient safety. By using this system we could ensure and document that 10 out of 11 canine patients had a total vector offset from the reference setup position<2mm immediately before and after irradiation. We have shown that SI can be used for surface guided FLASH-RT of canine patients. The SI system is currently not fast enough to interrupt a FLASH-RT beam while irradiating but with the short sampling time sudden motion can be detected. The beam can therefore be held just prior to irradiation, preventing treatment errors such as underdosing the target.

Bell-trap effectiveness to capture Crypturellus obsoletus and Crypturellus tataupa (Aves: Tinamidae) in southern Brazil

Specific techniques for the efficient capture of forest tinamous in a short sampling time are yet poorly described in the literature. Here, we report the efficiency of the bell-trap in the capture of Brown Tinamou (Crypturellus obsoletus) and Tataupa Tinamou (Crypturellus tataupa), in southern Brazil. We used three bell-traps adapted with casting nets. In 80 hours of sampling effort, four adult individuals of C. obsoletus were captured, in a forest area in the Pampa Biome. This study area is located in the municipality of São Sepé, state of Rio Grande do Sul, Brazil. Subsequently, in 16 hours of sampling effort in a forest area of the Atlantic Forest domain, one individual of C. obsoletus and one of C. tataupa were captured. This other study area is located in the municipality of Passo do Sobrado, RS. The bell-trap method has already been reported as efficient in capturing Yellow-legged Tinamous (Crypturellus noctivagus), in southern Brazil. However, since forest tinamous are cryptical birds, field adaptations are often necessary to achieve success in sampling activities. The use of this method proved efficient in the capture of forest tinamous, generating an important tool for the collection of bioecological data of this group of birds, which still represents a lack of studies.

Simulation of Time and Frequency Domain of Photosensitizers Effect During Light Transport through Tissue as Applied to Photodynamic Therapy using a Steady State Monte Carlo Method with Simulink

Photodynamic therapy (PDT) is a treatment process that makes use of a non-ionizing light source targeted at specific section of cells (a tumor) in order to induce apoptosis, and eventually, the death of cells in such area. This is achieved using specific light sensitive photosensitizing (PS) agents (in the case of this research Metvix-Methyl Aminolevulinate (MAL)) used to saturate the treatment area for a recorded time period before irradiating with non-ionizing light source. This research was designed to observe the time and frequency domains as the PS signal decays during the PDT process. During the process, the PS agent degenerates and becomes less and less interactive with the light source. Using the Hop/drop/spin nomenclature, the source of the incident photon is simulated using A Gaussian White Noise (AGWN) on Simulink and the incident signal is allowed to attenuate through the target area while the time and frequency domain of the photosensitizer MAL is recorded as it decays. It is observed that the amplitude of the absorption coefficient of the PS agent dropped to almost 0 during a short sample time while the normalized frequency decays with a highly damped and irregular oscillatory pattern. The improvement in attenuation due to the presence of the PS agent degenerates sharply during the initial state of the treatment in order to ensure that the generation of singlet oxygen terminates just after treatment time.

Unraveling the secrets of plant roots: Simplified method for large scale root exudate sampling and analysis in Arabidopsis thaliana

Background: Plants exude a plethora of compounds to communicate with their environment. Although much is known about above-ground plant communication, we are only beginning to fathom the complexities of below ground chemical communication channels. Studying root-exuded compounds and their role in plant communication has been difficult due to a lack of standardized methodologies. Here, we develop an interdisciplinary workflow to explore the natural variation in root exudate chemical composition of the model plant Arabidopsis thaliana. We highlight key challenges associated with sampling strategies and develop a framework for analyzing both narrow and broad scale patterns of root exudate composition in a large set of natural A. thaliana accessions. Methods: Our method involves cultivating individual seedlings in vitro inside a plastic mesh, followed by a short hydroponic sampling period in small quantities of ultrapure water. The mesh makes it easy to handle plants of different sizes and allows for large-scale characterization of individual plant root exudates in axenic conditions. This setup can also be easily extended for prolonged temporal exudate collection experiments. Furthermore, the short sampling time minimizes the duration of the experiment while still providing sufficient signal even with a small volume of sampling solution. We used ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) for untargeted metabolic profiling, followed by tentative compound identification using MZMine3 and SIRIUS 5 software, to capture a broad overview of root exudate composition in A. thaliana accessions. Results: Based on 28 replicates of Columbia genotype (Col-0) compared with 10 random biological controls, MZMine3 identified 354 metabolites to be present only in Col-0 by negative ionization. Of these, 313 compounds could be annotated by SIRIUS 5 software. Conclusions: The methodology developed in this study can be used to broadly investigate the role of root exudates as chemical signals in plant belowground interactions.

Evaluation of floral volatile patterns in the genus Narcissus using gas chromatography-coupled ion mobility spectrometry.

Daffodils (Narcissus, Amaryllidaceae) are iconic ornamentals with a complex floral biology and many fragrant species; however, little is known about floral plant volatile organic compounds (pVOCs) across the genus and additional sampling is desirable. The present study investigates whether the floral scent of 20 species of Narcissus can be characterized using gas chromatography-coupled ion mobility spectrometry (GC-IMS), with the aim of building a comparative pVOC data set for ecological and evolutionary studies. We used a commercial GC-IMS equipped with an integrated in-line enrichment system for a fast, sensitive, and automated pVOC analysis. This facilitates qualitative and (semi)-quantitative measurements without sample preparation. The GC-IMS provided detailed data on floral pVOCs in Narcissus with very short sampling times and without floral enclosure. A wide range of compounds was recorded and partially identified. The retrieved pVOC patterns showed a good agreement with published data, and five "chemotypes" were characterized as characteristic combinations of floral volatiles. The GC-IMS setup can be applied to rapidly generate large amounts of pVOC data with high sensitivity and selectivity. The preliminary data on Narcissus obtained here indicate both considerable pVOC variability and a good correspondence of the pVOC patterns with infrageneric classification, supporting the hypothesis that floral scent could represent a considerable phylogenetic signal.

An embedded Hildreth-based model predictive control of an Elbow Joint Orthosis Robot

In this paper, we consider a constrained model predictive control design for an Elbow Joint Orthosis Robot. The challenge of the robotic device control is to achieve passive and resistive rehabilitation movements with hard and soft constraints. A fast quadratic programming solver based on the Hildreth method is developed to meet the short real-time implementation sampling time. The online optimization solver based on the Hildreth method is compared with two other solvers proving its superiority in terms of computation time in the simulation environment. Simulation results show successful set point tracking and constraints satisfaction. An embedded version of constrained model predictive control using the Hildreth quadratic programming solver is implemented on an Arduino Due microcontroller. The results are satisfactory, and motion tracking errors and inputs variance are minimized. Less effort, optimal performances, and absence of overshoot and control signal oscillations are considered to evaluate the different studied motions. A lower computational cost is obtained taking into account the trade-off between the control effort and the closed-loop performances.

Advanced wastewater treatment with ozonation and granular activated carbon filtration: Inactivation of antibiotic resistance targets in a long-term pilot study

The inactivation of antibiotic resistant bacteria (ARB) and genes (ARGs) in an advanced plant combining ozonation and granular activated carbon (GAC) filtration applied for effluent after conventional activated sludge treatment at a full-scale urban wastewater treatment plant was investigated for over 13 consecutive months. The nitrite compensated specific ozone dose ranged between 0.4 and 0.7 g O3/g DOC with short-time sampling campaigns (0.2–0.9 g O3/g DOC). Samples were analysed with culture-dependent methods for bacterial targets and with qPCR for genes. The log removal values were correlated with a decrease of the matrix UV absorption at 254 nm (ΔUV254) and indicated a range of ΔUV254 that corresponds to a sufficient membrane damage to affect DNA. For trimethoprim/sulfamethoxazole resistant E. coli, sul1, ermB and tetW, this phase was observed at ΔUV254 of ~30 % (~0.5 g O3/g DOC). For ampicillin resistant E. coli and blaTEM-1, it was observed around 35–40 % (~0.7 g O3/g DOC), which can be linked to mechanisms related to oxidative damages in bacteria resistant to bactericidal antibiotics. GAC treatment resulted in a further abatement for trimethoprim/sulfamethoxazole E. coli, sul1 and tetW, and in increase in absolute and relative abundance of ermB and blaTEM-1.

CARBENDAZIM RESIDUES QUANTIFICATION IN GREEN LETTUCE BY ENZYME-LINKED IMMUNOSORBENT ASSAY

Carbendazim is a fungicide that is not approved for use in the European Union, but is used in some other countries to preserve agricultural crops. Enzyme-linked immunosorbent assay (ELISA) become a viable alternative to traditional instrumental methods in pesticides analysis, for screening purpose. Carbendazim residues were determined in two green lettuce cultivars, from the experimental lots, harvested at different times after the treatment with 0.1 % carbendazim solution, by an ELISA carbendazim commercial kit and the values were compared with those obtained by high performance liquid chromatography (HPLC). The results of ELISA and HPLC analysis of carbendazim residues in the two green lettuce cultivars were well correlated (Pearson correlation coefficient r=0.8925). Carbendazim residues in green lettuce/vegetables can be determined with high reliability by commercial ELISAs with results well correlated with those obtained by HPLC, with an additional sample preparation step involving solid phase extraction, in order to reduce matrix effects; the assay can be used for carbendazim screening of many vegetable/fruit samples in shorter time and with inexpensive/cheaper equipment.

The effect of exercise training on adipose tissue insulin sensitivity: A systematic review and meta-analysis.

This systematic review and meta-analysis determined the impact of exercise training on adipose tissue insulin sensitivity in adults. Its scope extended to studies measuring whole-body and localized subcutaneous adipose tissue insulin sensitivity using validated techniques. Consensus from four studies demonstrates that exercise training improved whole-body adipose tissue insulin sensitivity when measured via stable-isotope lipid tracers (rate of appearance suppression in response to hyperinsulinemia). Meta-analysis of 20 studies (26 intervention arms) employing the adipose tissue insulin resistance index (ADIPO-IR) supported these findings (-10.63 [-14.12 to -7.15] pmol·L-1 × mmol·L-1 ). With ADIPO-IR, this response was greater in studies documenting weight loss and shorter sampling time (≤48 h) post-training. Overall, exercise training did not affect whole-body adipose tissue insulin sensitivity in seven studies (11 intervention arms) measuring the suppression of circulating non-esterified fatty acids in response to insulin infusion (1.51 [-0.12 to 3.14]%); however, subgroup analysis identified an enhanced suppression post-training in trials reporting weight loss. From four microdialysis studies, consensus indicates no effect of exercise training on localized (abdominal/femoral) adipose tissue insulin sensitivity, potentially suggesting that enhanced whole-body responses are related to improvements in central adipose depots. However, heterogeneity within microdialysis protocols dictates that findings must be viewed with caution.