Monitoring Of Profiles Research Articles

Real-time continuous monitoring of dynamic concentration profiles studied with biosensing by particle motion.

Real-time monitoring-and-control of biological systems requires lab-on-a-chip sensors that are able to accurately measure concentration-time profiles with a well-defined time delay and accuracy using only small amounts of sampled fluid. Here, we study real-time continuous monitoring of dynamic concentration profiles in a microfluidic measurement chamber. Step functions and sinusoidal oscillations of concentrations were generated using two pumps and a herringbone mixer. Concentrations in the bulk of the measurement chamber were quantified using a solution with a dye and light absorbance measurements. Concentrations near the surface were measured using a reversible cortisol sensor based on particle motion. The experiments show how the total time delay of the real-time sensor has contributions from advection, diffusion, reaction kinetics at the surface and signal processing. The total time delay of the studied real-time cortisol sensor was ∼90 seconds for measuring 63% of the concentration change. Monitoring of sinusoidal cortisol concentration-time profiles showed that the sensor has a low-pass frequency response with a cutoff frequency of ∼4 mHz and a lag time of ∼60 seconds. The described experimental methodology paves the way for the development of monitoring-and-control in lab-on-a-chip systems and for further engineering of the analytical characteristics of real-time continuous biosensors.

Monitoring of digital literacy levels and profiles in the regional education system: analysis of digital gaps

Introduction. Digital literacy (hereinafter – DL) is a key element of the modern educational system, and its absence may lead to limited access to educational resources and unequal development opportunities. Monitoring of DL levels of pupils and students in the system of general (hereinafter – GE) and secondary vocational education (hereinafter – SVE) allows to identify strengths and weaknesses of digital training in the regional education system and to put forward cause-and-effect investigative hypotheses about the causes of digital breaks. The purpose of the research is to investigate levels and profiles of digital literacy among students in different educational institutions and to identify digital gaps in different parameters. Materials and methods. More than three thousand students from various types of educational institutions took part in the study, including general education schools, gymnasiums, lyceums and colleges of the Tomsk region (Russian Federation). The monitoring process used a previously developed operationalized model of DL adapted for the Russian education system. Methods of mathematical statistics (Kruskal — Wallis, Mann–Whitney, One-way analysis of variance) were used to compare levels and profiles of DL at selected levels of GE and SVE. Results. There are significant differences in DL between the level of GE as a whole and SVE (z = -3,824, p < 0,0001; z = -2,904, p < 0,004). Among the students of GE and SVE the highest levels of metacompetencies of information literacy and content creation (primary general education (hereinafter – PGE)– 64%, basic general education (hereinafter – BGE) – 66.8%, secondary general education (hereinafter – SGE) – 71.2% and SVE – 71.4%), the lowest – level of metacompetency of interaction and communication in networks (PGE – 73%, BGE – 50.2%, SGE – 66.8% and SVE – 59.8%). Conclusion. The monitoring of digital literacy in the regional system of general and secondary vocational education has shown that there are many gaps and disparities both within individual digital literacy profiles and at different stages and levels of education. DL gaps among learners are caused by gaps in digital training at educational institutions, unequal access to infrastructure, and socio-economic and cultural inequalities. The analysis of the digital divide will lead to the development of recommendations and strategies aimed at bridging the DL gap.

Amperometric Measurements by a Novel Aerosol Jet Printed Flexible Sensor for Wearable Applications

In the last years, increasing interest has been addressed on wearable devices for the continuous monitoring of biochemical profiles of patients and athletes. Among various markers, lactate represents one of the most interesting to deepen the knowledge on fatigue processes, in combination with traditional electromyographic features. In this work, we propose a new, low-cost, rapid, and flexible electrochemical amperometric lactate carbon biosensor fabricated by the novel technique of aerosol jet printing (AJP). The developed biosensors were combined with a paper-based microfluidic system and a chitosan-based stable functionalization to allow a long-term continuous monitoring of human daily activities. The sensor performances were evaluated by static and dynamic electrochemical tests in buffered media. The limit of detection is 11 ± 2 mM for the static setup and of 15 ± 6 mM for the dynamic setup, while sensitivity is 0.02 and 0.04 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> /mM, respectively, with single sigma standard deviation lower than 8%. Increasing and decreasing concentration steps of lactate induces comparable response times below 30 s. These results obtained in vitro under controlled laboratory conditions represent a promising starting point to pursue with ex-vivo and in vivo validation for the future development of stand-alone wearable patches for noninvasive lactate measurements.

Association between ustekinumab therapy and changes in specific anti-microbial response, serum biomarkers, and microbiota composition in patients with IBD: A pilot study.

Ustekinumab, is a new therapy for patients with IBD, especially for patients suffering from Crohn's disease (CD) who did not respond to anti-TNF treatment. To shed light on the longitudinal effect of ustekinumab on the immune system, we investigated the effect on skin and gut microbiota composition, specific immune response to commensals, and various serum biomarkers. We recruited 11 patients with IBD who were monitored over 40 weeks of ustekinumab therapy and 39 healthy controls (HC). We found differences in the concentrations of serum levels of osteoprotegerin, TGF-β1, IL-33, and serum IgM antibodies against Lactobacillus plantarum between patients with IBD and HC. The levels of these biomarkers did not change in response to ustekinumab treatment or with disease improvement during the 40 weeks of observation. Additionally, we identified differences in stool abundance of uncultured Subdoligranulum, Faecalibacterium, and Bacteroides between patients with IBD and HC. In this preliminary study, we provide a unique overview of the longitudinal monitoring of fecal and skin microbial profiles as well as various serum biomarkers and humoral and cellular response to gut commensals in a small cohort of patients with IBD on ustekinumab therapy.

Dynamics of Mixing Layer Height and Homogeneity from Ceilometer-Measured Aerosol Profiles and Correlation to Ground Level PM2.5 in New York City

Observations of mixing layer height (MLH), its dynamics and the extent of homogeneity of the mixing layer are important in the study of air pollution as well as meteorological and air quality model validation. They can contribute to improvements in the application of satellite aerosol optical depth (AOD) products to ground fine particulate matter (PM2.5) estimation that can potentially provide synoptic-scale monitoring of fine particulate matter. Ceilometers have shown great potential for continuous monitoring of MLH and aerosol profiles within the boundary layer. In this study, we report an automated quality control/quality assurance (QC/QA) method that improves the consistency of MLH retrievals from ceilometer measurements and present measurements of MLH variation in New York City (NYC) as observed by ceilometers in summer and winter seasons. Distinct issues due to SNR and quality of overlap correction are addressed within the QC/QA method. We also analyze the diurnal and seasonal correlations between ceilometer-attenuated backscatter and ground-level PM2.5 as a function of height, time of day, and season, to shed light on the homogeneity of aerosol vertical mixing within the MLH, as well as the correlation between aerosol optical properties and PM2.5. The results show that the overall correlation in summer is better than in winter. This correlation decreases with increasing height but the degradation is less severe in summer than in winter, which is qualitatively consistent with urban heating models of convective mixing. However, no significant diurnal variation of the correlation coefficient was observed for both seasons. We also found that the linear regression slope between ceilometer-attenuated backscatter coefficients and ground PM2.5 shows seasonal variation, which can be partially explained by the difference in aerosol size distribution and aerosol species between summer and winter. Finally, we investigated the homogeneity of aerosol vertical distribution within the mixing layer (ML) during the daytime. The results indicate that the aerosols are well-mixed within the lower part of ML up to 500 m.

Monitoring of Linear Profiles Using Linear Mixed Model in the Presence of Measurement Errors

In the application of control charts, most of the research in profile monitoring is based on accurate measurements. Measurement errors, however, often exist in many manufacturing and service environments. In this paper, we apply linear mixed models in the presence of measurement errors in fixed effects. We discuss three modified multivariate charts, namely Hotelling’s T2, multivariate exponential weighted moving average (MEWMA) control chart, and multivariate cumulative sum (MCUSUM) control chart. Performance comparisons are made in terms of the average run length (ARL) and average extra quadratic loss (AEQL). Finally, a real data example on healthcare expenditures is used to illustrate the implementation of the proposed monitoring schemes.

Epidemiology and antibiotic susceptibility of multidrug-resistant Staphylococcus aureus in nosocomial and community acquired infections in southeastern Algeria

Bacterial nosocomial and community acquired infections due to the emergence of drug-resistant pathogens cause high morbidity and mortality worldwide in humans. Staphylococci are number one among the Gram-positive bacteria responsible for multiple infections in hospitals. In this survey, we determined the epidemiological aspects of Multi-drug-resistant – Methicillin resistant Staphylococcus aureus (MDR-MRSA) strains as well as antimicrobial susceptibility profile to different families of antibiotics. 80 MDR-MRSA strains were isolated from different pathological origins, during the period from August 2019 – August 2020. The isolation and the identification of S.aureus strains were based on conventional methods. Methicillin resistance was verified for each strains by using a disc of oxacillin (5μg) or cefoxitin (30μg) .The antibiotic susceptibility testing was performed by disc diffusion method on Mueller Hinton agar and evaluation of minimal inhibitory concentrations (MICs), according to Clinical &amp; Laboratory Standards Institute (CLSI). The study has shown that 80 MRSA strains were identified among the 125 S. aureus strains isolated (64 %). The majority of staphylococci come from pus (67,5%) followed by the blood culture (27,5%). 31,25% come mostly from intensive care unit with a percentage of 72% in adults, including 62,5% of cases recorded in females. The MRSA strains show multidrug resistance to different antibiotic families. We identified seven MRSA strains expressing reduced glycopeptides susceptibility. MRSA is important and serious pathogens in human infections, continued monitoring of susceptibility profiles of these pathogens to important antibiotics is required for a better therapeutic approach against nosocomial and community acquired infections caused by MDR-MRSA.

Quantitative and qualitative evaluation of the use of nonsteroidal anti-inflammatory drugs in the Russian Federation over 10 years

Relevance. Studies devoted to the assessment of consumption, as well as to the issues of pharmacosafety of nonsteroidal anti-inflammatory drugs, are limited at the national level in the Russian Federation. The study of these issues will determine the general trends of consumption and prescription of this group of drugs.Aim. To assess the consumption pattern of NSAIDs in the Russian Federation in the period from 2010 to 2020.Methods. Information was searched using data from the state register of drugs (GRLS) as well as national database downloads (automated information system of Roszdravnadzor; AIS RZN). To estimate the consumption of the main INN from the group of NSAIDs for the period 2010–2020 the information on the sales volume of all trade names, sold for sale in the Russian Federation was unloaded from the database of IQVIA Solutions Ltd. and analyzed.Results. The analysis showed that the first place by total amount of consumption was occupied by acetylsalicylic acid (5 953 058 854); second place — paracetamol (4 635 122 085); third place — ibuprofen (3 230 415 088); fourth place — metamizole (4 172 344 008); fifth place — ketorolac (91 617 809,04); sixth — nimesulide (69 761 7481,8). The claimed INNs showed a slightly different distribution when calculating the DDDs/P (patient-year) totals, namely, in descending order, nimesulide, ketorolac, ibuprofen, acetylsalicylic acid, paracetamol, and methamisole.Conclusion. The indicated levels of consumption do not correlate with the data of the national database “Pharmacovigilance”, which indicates insufficient post-marketing monitoring of the safety profiles of the indicated medicines.

Real-Time Battery Temperature Monitoring Using FBG Sensors: A Data-Driven Calibration Method

Battery storage has an important role to play in integrating large-scale renewable power generations and in transport decarbonization. Real-time monitoring of battery temperature profiles is indispensable for battery safety management. Due to the advantages of small size, resistance to corrosion, immunity to electromagnetic interference, and multiplexing, fiber Bragg-grating (FBG) sensing has received substantial interest in recent years for battery temperature measurement. However, traditional temperature calibration for FBG sensors often requires a high-standard reference and causes the sensors to fail to be consistent during the calibration or recalibration processes. To tackle the challenges, an ensemble data-driven calibration method is developed in this article for FBG sensors. The calibration model consists of a linear part and a nonlinear part. First, the fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> -means (FCM) algorithm is used to extract the linear relationship between the measured FBG wavelength shift and temperature variation. Then, the empirical mode decomposition (EMD) technique is used to classify the intrinsic mode functions (IMFs) and the remainder of the unmodeled nonlinear information. The unmodeled nonlinear information is further compensated using battery state of charge (SOC) and cycle number information. The experimental results confirm that the proposed temperature calibration method achieves desirable accuracy and reliability, with both the mean absolute error (MAE) and root mean square error (RMSE) being around 0.2 °C, respectively. Compared with the traditional temperature calibration method, the proposed approach can be used online in real-life applications.

In-situ monitoring of Material Extrusion processes via thermal videoimaging with application to Big Area Additive Manufacturing (BAAM)

Material Extrusion (ME) is becoming one of the most promising class of Additive Manufacturing (AM) processes in the manufacturing panorama for its capability of realizing products with complex shapes, potentially in large dimensions, for a variety of applications and materials for different industrial sectors. Despite the significant advances in material range and machine capabilities, the lack of process stability and repeatability of ME still represents an obstacle for the adoption of this process on large industrial scales. Following the success of in-situ monitoring in other AM technologies, some studies are investigating the possibility of embedding in-situ sensors on commercial machines. Among them, thermal infrared monitoring systems have gained lots of interest for the crucial role of temperature evolution in final part quality and mechanical performance. This study presents a data-driven methodology for in-situ monitoring of thermal profiles. The proposed algorithm extracts, models, and compares the cooling history at different locations. By combining a tracking algorithm and automatic detection of region of interests and robust regression for functional data modeling, we propose a solution to study the cooling history of the 3D printed geometry as the process evolves, to draw general conclusions on the process and detect anomalous thermal profiles, which may entail lack of bonding or other flaws. This paper further proposes a novel approach to automatically detect local temperature inhomogeneities, also known as “hot” and “cold spots”, which are often symptomatic of critical defects in the 3D printed part. In-situ detection of hold- and cold-spots can be particularly useful for large scale products or functional products printing. The proposed methods can be applied to any ME process, but it is specifically applied to a real case study of Big Area Additive Manufacturing (BAAM), in collaboration with the Oak Ridge National Laboratory. Results show the effectiveness of all the proposed solutions and pave the way towards zero-defects production.

Branching fraction measurement of the prompt-NO switch reaction NCN + H

The branching fraction ϕ of the prompt-NO switch reaction NCN + H (1), essentially yielding either CH + N2 (1a) or HCN + N (1b), is key for modeling prompt-NO formation in flames. Large discrepancies exist between available experimental data and flame modeling studies on the one hand and high-level theoretical predictions on the other hand, resulting in a factor of 5 uncertainty for ϕ1b at T=1500 K. By simultaneous monitoring of NCN and HCN concentration-time profiles during the reaction NCN + H at temperatures 1200K<T<2000K behind shock waves, ϕ1b has been directly measured for the first time. Thermal decomposition of cyanogen azide (NCN3) and ethyl iodide (C2H5I) served as sources for NCN radicals and H atoms. NCN has been detected by UV laser absorption at 329.130 nm and HCN detection was accomplished by mid-infrared frequency modulation spectroscopy at 3228.049cm−1. Kinetic simulations provided both the total rate constant k1 from the NCN and k1b from the HCN profiles. In order to account for a side reaction of residual BrCN from NCN3 synthesis, a rough estimate for the rate constant of the reaction BrCN + H was inferred from the experiments as well. The measured rate constants for k1 confirm earlier results (Faßheber et al., Phys. Chem. Chem. Phys. 16 (2014) 11647) and k1b follows the Arrhenius expression k1b/(cm3mol−1s−1)=4.2×1014exp(−38.2kJmol−1/RT) with moderate uncertainties of ±(37−49%). The branching fraction increases with temperature, yielding ϕ1b=0.22(±46%) at 1200 K, 0.47(±42%) at 1600 K, and 0.63(±53%) at 2000 K. The corresponding prompt-NO switch temperature of TS=1670K (ϕ1b=0.5) shows that the reaction NCN + H advances toward the Fenimore products HCN + N already at typical temperatures of hydrocarbon/air flames, in stark contrast to the most recent theoretical estimate reporting TS=3235K.

Microfluidic chip interfacing microdialysis and mass spectrometry for in vivo monitoring of nanomedicine pharmacokinetics in real time

Although liposomes have demonstrated significant clinical success as drug delivery vehicles, pharmacokinetic (PK) profiling of liposomal nanomedicines remains difficult due to technical challenges accurately measuring low concentrations of free drug in complex biological matrices. Microdialysis (MD) is well established as a powerful in vivo sampling tool for PK studies, but non-volatile salts present in the microdialysate are incompatible with mass spectrometry (MS) analysis without tedious sample pre-treatment. To address this issue, a µSPE-based microfluidic chip was fabricated to interface MD with MS. By incorporating PEG 20,000 as an effective anti-foulant, the µSPE-based microfluidic chip demonstrated excellent efficiencies in drug extraction and de-salting of the microdialysate, providing a promising approach to real-time monitoring of nanomedicine PK profiles.

Genotypic characterization of mecA gene and antibiogram profile of coagulase-negative staphylococci in subclinical mastitic cows.

Coagulase-negative staphylococci (CNS) are becoming the major cause of clinical and subclinical bovine mastitis around the world. This study aims to estimate the prevalence, antibiogram, and frequency of the methicillin-resistant (MR) (mecA) gene in CNS collected from cows with subclinical mastitis. Thirty-four milk samples were collected from 20 cows. Fifteen subclinical mastitis samples (~44.12%) were identified as CNS isolates. The Vitek2 compact system method was employed for the identification of the species. Furthermore, antibiotic sensitivity tests were performed against 10 different antibiotics for CNS strains. The mecA gene from isolated CNS was detected by conventional polymerase chain reaction (PCR). Staphylococcus haemolyticus was the most predominant isolated species with an incidence of 33.3% (5/15 isolates), followed by 26.7% for Staphylococcus sciuri and Staphylococcus vitamins (4/15 isolates), and 13.3% for Staphylococcus vitulinus (2/15 isolates), respectively. The highest resistance rates were determined to be 40% (6/15 isolates) against penicillin and oxacillin (OX), 33.3% (5/15 isolates) against clindamycin, 13% (2/15 isolates) against chloramphenicol, amoxicillin, and erythromycin, and 5% (1/15 isolates) against ciprofloxacin, respectively. The results revealed that the isolates were resistant to one or more antimicrobial agents, with five isolates displaying multiple antimicrobial resistance. Furthermore, the results exhibit that all CNS isolates had the mecA gene at 310 bp with a 100% frequency. Moreover, for detecting MR isolates, there are significant discrepancies between phenotypic and genotypic approaches, and only 6/15 CNS isolates phenotypically demonstrated OX resistance. The results emphasize the necessity of frequent monitoring of phenotypic and genotypic profiles of CNS isolates to ensure effective control measures and the prevention of multidrug resistance strain evolution.

Relationship of serum lipid profiles in preeclampsia and normal pregnancy, Bangladesh.

The purpose of this present study to investigate the lipid profile levels and basal body mass index in preeclampsia and normal pregnancy in Bangladeshi women. This case-control study was conducted at Sheba Hospital Kaligonj Bangladesh with 70 participants among 35 normal pregnancy (control) and 35 preeclampsia women (case) were enrolled from August 2018 to July 2019. Blood samples were obtained for analysis of total cholesterol, triglyceride and high-density lipoprotein by enzymatic assays and low-density lipoprotein by using Fried Ewald's formula in between 22-36 weeks of gestation. This study found the mean age of preeclampsia and normal pregnancy women were 24.71±2.56 and 23.09±2.1 respectively with significant (P= 0.005). Basal body mass index, total cholesterol, triglyceride and low-density lipoprotein significantly higher (P=0.002), (P= 0.000), (P= 0.022) and (P=0.000) in preeclampsia compared to normal pregnancy respectively. While high-density lipoprotein comparatively lower in preeclampsia than normal pregnancy and consider significant as (P=0.037). Abnormal lipid profile and increased body mass index is contributed to the development of preeclampsia. The frequent antenatal monitoring of lipid profiles provides the status which helps to require management and reduces the preeclampsia which enhances maternal and fetal wellbeing and fetal outcome.

Online monitoring of profiles via function‐on‐scalar model with an application to industrial busbar

AbstractIn Statistical Process Control, profile monitoring is used for monitoring whether the process is in‐control or not by checking the relationship stability between the process response and covariates. In the industrial process recently, the functional relationship between the process variables is more and more complex, and the profile data are often time‐dependent and subject to within‐profile autocorrelation. We need to use appropriate models to characterize these complex relationships, and design new profile monitoring schemes to ensure satisfactory process monitoring performance. This paper studies a practical nonparametric profile monitoring problem, in which the functional relationship is in the form of function‐on‐scalar and within‐profile data are autocorrelated. A new online profile monitoring scheme is proposed to monitor process anomalies in real time. In Phase Ⅰ, based on the in‐control historical data, the model for the process is established estimated. Then, using the monitoring statistics based on penalty spline smoothing, the function‐on‐scalar monitoring parameters control scheme with a moving window is presented, and is applied to the anomaly monitoring of online sequential profile data. A large number of numerical simulation results show that in the presence of the function‐on‐scalar relationship, the proposed scheme has a superior performance in various cases. Finally, an application example of industrial busbar running process monitoring is given to illustrate the effectiveness of the scheme.

Cooking and pasteurizing evaluation of barramundi (Lates calcarifer) meats subjected to an emerging microwave-assisted induction heating (MAIH) technology

Microwave-assisted induction heating (MAIH) is a complex heating process characterized by rapid and uniform heating based on a combination of microwave heating (power: 1000 W; frequency: 2450 MHz) applied from above and electromagnetic induction heating (power: 1500 W) applied from below. In this study, we assessed the utility of MAIH in processing the flesh of barramundi (Lates calcarifer), an important aquaculture species in Asian countries. To determine the effects of different heating conditions on the microbial contamination and physicochemical qualities of barramundi meat, fresh barramundi meats (100 g) were sealed in heat-resistant crystallized polyethylene terephthalate containers containing 150 mL of saline solution, and were subsequently heated in MAIH to 90 °C or 70 °C and held to different time. The results revealed that in response to an increase in the duration of heating, there were reductions in the aerobic plate count, psychrotrophic bacteria count, hydrogen sulphide-producing bacteria count, and coliform count, along with lower contents of total volatile basic nitrogen, in treated samples. Conversely, with a prolongation of the time exposed to heat, we detected increases in the L* (lightness),W (whiteness), ΔE(colour difference), and texture (hardness and chewiness) of fish meat, whereas there was a reduction of a* (redness). When the samples were heated for at least 110 s to 90 °C or for at least 130 s to 70 °C, the barramundi meats obtained appeared to be fully cooked with microbial counts below the limits of detection. In addition, under both heating conditions, thermal imaging revealed a uniform distribution of temperature within treated samples. Furthermore, monitoring of temperature profiles at central points in fish samples revealed that maximum temperatures of 97 °C and 79 °C were reached in response to MAIH processing at 90 °C for 110 s and 70 °C for 130 s, respectively. Thus, on the basis of the quality considerations of appearance, microbial count, and texture, the recommended optimal heating conditions for barramundi meats produced using MAIH are 90 °C for 110 s and 70 °C for 130 s. Industrial relevanceThe MAIH equipment is a novel thermal processing technology and allows food to be heated and pasteurized simultaneously after the package is sealed, eliminates issues of secondary contamination due to the need for subsequent packaging, and extends the shelf life. MAIH technology, therefore, serves as a viable future alternative for thermal processing of manufacturing refrigerated ready-to-eat seafood.

Altered subgenomic RNA abundance provides unique insight into SARS-CoV-2 B.1.1.7/Alpha variant infections

B.1.1.7 lineage SARS-CoV-2 is more transmissible, leads to greater clinical severity, and results in modest reductions in antibody neutralization. Subgenomic RNA (sgRNA) is produced by discontinuous transcription of the SARS-CoV-2 genome. Applying our tool (periscope) to ARTIC Network Oxford Nanopore Technologies genomic sequencing data from 4400 SARS-CoV-2 positive clinical samples, we show that normalised sgRNA is significantly increased in B.1.1.7 (alpha) infections (n = 879). This increase is seen over the previous dominant lineage in the UK, B.1.177 (n = 943), which is independent of genomic reads, E cycle threshold and days since symptom onset at sampling. A noncanonical sgRNA which could represent ORF9b is found in 98.4% of B.1.1.7 SARS-CoV-2 infections compared with only 13.8% of other lineages, with a 16-fold increase in median sgRNA abundance. We demonstrate that ORF9b protein levels are increased 6-fold in B.1.1.7 compared to a B lineage virus in vitro. We hypothesise that increased ORF9b in B.1.1.7 is a direct consequence of a triple nucleotide mutation in nucleocapsid (28280:GAT > CAT, D3L) creating a transcription regulatory-like sequence complementary to a region 3’ of the genomic leader. These findings provide a unique insight into the biology of B.1.1.7 and support monitoring of sgRNA profiles to evaluate emerging potential variants of concern.

Monitoring of two-dimensional tungsten concentration profiles on the HL-2A tokamak

In this article, we demonstrate the inference of two-dimensional tungsten concentration profiles in tokamak plasmas, using Gaussian process tomography applied to bolometry and assuming a specific model for the tungsten cooling factor. In ITER, tungsten has been selected for divertor material due to its low tritium retention and ability to handle large heat and particle flux loads. On the other hand, this will cause tungsten impurities to enter the bulk plasma through various plasma-wall interaction processes. Therefore, a detailed understanding of tungsten impurity transport and active control of the impurity transport in tokamaks is crucial. The computational complexity of the method described in their article compares favorably to a simple least-squares approach , n represents the number of pixels and m the number of measurement channels, hence bringing real-time tungsten profile monitoring within reach (<10 ms repetition time). The feasibility study of this method has been demonstrated here to a discharge in HL-2A for observing the entire process of tungsten impurities entering the bulk plasma from the scrape-off layer area, tungsten pump-out by edge-localized modes, as well as the formation of a poloidally asymmetric tungsten distribution.

Metabolomic fingerprinting of porcine lung tissue during pre-clinical prolonged ex vivo lung perfusion using in vivo SPME coupled with LC-HRMS

Normothermic ex vivo lung perfusion (NEVLP) has emerged as a modernized organ preservation technique that allows for detailed assessment of donor lung function prior to transplantation. The main goal of this study was to identify potential biomarkers of lung function and/or injury during a prolonged (19 h) NEVLP procedure using in vivo solid-phase microextraction (SPME) technology followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). The use of minimally invasive in vivo SPME fibers for repeated sampling of biological tissue permits the monitoring and evaluation of biochemical changes and alterations in the metabolomic profile of the lung. These in vivo SPME fibers were directly introduced into the lung and were also used to extract metabolites (on-site SPME) from fresh perfusate samples collected alongside lung samplings. A subsequent goal of the study was to assess the feasibility of SPME as an in vivo method in metabolomics studies, in comparison to the traditional in-lab metabolomics workflow. Several upregulated biochemical pathways involved in pro- and anti-inflammatory responses, as well as lipid metabolism, were observed during extended lung perfusion, especially between the 11th and 12th hours of the procedure, in both lung and perfusate samples. However, several unstable and/or short-lived metabolites, such as neuroprostanes, have been extracted from lung tissue in vivo using SPME fibers. On-site monitoring of the metabolomic profiles of both lung tissues through in vivo SPME and perfusate samples on site throughout the prolonged NEVLP procedure can be effectively performed using in vivo SPME technology.

Characteristics of Seepage Barriers in Fluvial Reservoirs of Meandering Rivers and Their Impacts on Water Injection Development: A Case Study of Neogene Guantao Formation in Block M, Gudao Oilfield

To deepen our understanding of reservoir heterogeneity, seepage barriers in the study area were divided into interlayer, intralayer, and planar levels based on their spatial distribution characteristics and then investigated by taking the third member of the Neogene Guantao Formation in the block M of the Gudao oilfield as an example. Based on their genesis characteristics, the interlayer seepage barriers were divided into the overbank sand-overbank sand type, overbank sand-channel type, isolated channel superposition type, and channel shallow-cut type. The planar seepage barriers were categorized into the channel boundary type, abandoned channel type, channel-overbank sand type, and floodplain mudstone type. The intralayer seepage barriers were classified into the mudstone type and physical property type. Classifying multiple levels of different types of seepage barriers led to the refinement of their spatial characteristics. The strength of seepage barriers was characterized using the “seepage barrier coefficient” and “reservoir quality coefficient,” and the small-valued coefficients of the interlayer and intralayer seepage barriers indicated that these seepage barriers are characterized by large thickness, high mudstone content, poor physical properties, and weak seepage ability. The strength of the planar seepage barriers was dependent on the sedimentary facies types and channel stages, and the difference between planar seepage barriers was characterized using the reservoir quality coefficient. The seepage barriers were described based on the multilevel classification of seepage barrier categories, the intralayer seepage barriers with a lateral accretion pattern in the meandering river point bars were identified and described, and the distribution characteristics of seepage barriers were summarized at multiple levels. The method of “hierarchical analysis, skeleton construction, and category fitting” was used to establish 3D models of different levels of seepage barriers, and a modeling method based on the characteristics of intralayer seepage barriers under the constraint of architecture pattern was used to model intralayer seepage barriers with a lateral accretion pattern. In the monitoring of water injection profiles, it was found that the extent of blockage achieved by seepage barriers affects the water injection volume and thus controls the fluid transport pattern. Due to the development characteristics of the seepage barrier in the formation, there are some differences in injection production efficiency under different well pattern matching modes. The research on the distribution characteristics of different levels of seepage barrier categories provides a reliable geological basis for improving the injection-production relationship.