Numerous Samples Research Articles

Assessment of pDNA isoforms using microfluidic electrophoresis.

The recent rise in nucleic acid-based vaccines and therapies has resulted in an increased demand for plasmid DNA (pDNA). As a result, there is added pressure to streamline the manufacturing of these vectors, particularly their design and construction, which is currently considered a bottleneck. A significant challenge in optimizing pDNA production is the lack of high-throughput and rapid analytical methods to support the numerous samples produced during the iterative plasmid construction step and for batch-to-batch purity monitoring. pDNA is generally present as one of three isoforms: supercoiled, linear, or open circular. Depending on the ultimate use, the desired isoform may be supercoiled in the initial stages for cell transfection or linear in the case of mRNA synthesis. Here, we present a high-throughput microfluidic electrophoresis method capable of detecting the three pDNA isoforms and determining the size and concentration of the predominant supercoiled and linear isoforms from 2 to 7kb. The limit of detection of the method is 0.1ng/µL for the supercoiled and linear isoforms and 0.5ng/µL for the open circular isoform, with a maximum loading capacity of 10-15ng/µL. The turnaround time is 1min/sample, and the volume requirement is 10µL, making the method suitable for process optimization and batch-to-batch analysis. The results presented in this study will enhance the understanding of electrophoretic transport in microscale systems dependent on molecular conformations and potentially aid technological advances in diverse areas relevant to microfluidic devices.

Deep eutectic solvent-based shaking-assisted extraction for determination of bioactive compounds from Norway spruce roots.

Polyphenolic compounds play an essential role in plant growth, reproduction, and defense mechanisms against pathogens and environmental stresses. Extracting these compounds is the initial step in assessing phytochemical changes, where the choice of extraction method significantly influences the extracted analytes. However, due to environmental factors, analyzing numerous samples is necessary for statistically significant results, often leading to the use of harmful organic solvents for extraction. Therefore, in this study, a novel DES-based shaking-assisted extraction procedure for the separation of polyphenolic compounds from plant samples followed by LC-ESI-QTOF-MS analysis was developed. The DES was prepared from choline chloride (ChCl) as the hydrogen bond acceptor (HBA) and fructose (Fru) as the hydrogen bond donor (HBD) at various molar ratios with the addition of 30% water to reduce viscosity. Several experimental variables affecting extraction efficiency were studied and optimized using one-variable-at-a-time (OVAT) and confirmed by response surface design (RS). Nearly the same experimental conditions were obtained using both optimization methods and were set as follows: 30mg of sample, 300mg of ChCl:Fru 1:2 DES containing 30%w/w of water, 500rpm shaking speed, 30min extraction time, 10°C extraction temperature. The results were compared with those obtained using conventional solvents, such as ethanol, methanol and water, whereby the DES-based shaking-assisted extraction method showed a higher efficiency than the classical procedures. The greenness of the developed method was compared with the greenness of existing procedures for the extraction of polyphenolic substances from solid plant samples using the complementary green analytical procedure index (ComplexGAPI) approach, while the results for the developed method were better or comparable to the existing ones. In addition, the practicability of the developed procedure was evaluated by application of the blue applicability grade index (BAGI) metric. The developed procedure was applied to the determination of spruce root samples with satisfactory results and has the potential for use in the analysis of similar plant samples.

Competing Endogenous RNAs (ceRNAs) and Application of Their Regulatory Networks in Complex Traits and Diseases of Ruminants

This manuscript summarizes information on the diverse range of RNA molecules and their role as competing endogenous RNAs (ceRNAs). Moreover, it provides an overview of ceRNA regulatory networks and their applications in ruminant biology. Knowledge of co-expression networks has increased with microarrays, RNA-seq, and scRNA-seq characterizing molecular mediators across various biological scales, using sequences from numerous blood and tissue samples. By synthesizing existing knowledge, this study summarizes interactions between coding and non-coding RNAs through microRNA response elements (MREs), elucidating large-scale regulatory networks throughout the transcriptome that influence the expression and activities of various ceRNAs. Identification of non-coding RNAs with important regulatory functions will revolutionize understanding of RNA biology, shifting from an mRNA-centric model to a complex network of RNA crosstalk. The ceRNA networks offer a more comprehensive and arguably more realistic perspective compared to protein–protein interaction (PPI) networks and weighted gene co-expression networks (WGCN). These ceRNA regulatory networks can describe potential molecular regulatory mechanisms related to functional and economically important traits in ruminants, plus contribute to disease and pathology research, by elucidating pathogenesis and potential drug effects in disease and cancer models. Furthermore, they can provide insights into farm animal biology, e.g., reproductive traits in goats and sheep, regulation of fat metabolism in beef cattle, heat stress responses, and lactation regulation in dairy cattle, fertility and muscle characteristics in buffalo, and resistance to high-salt and water-deprivation conditions in camels. In conclusion, ceRNA and associated regulatory networks should promote a new understanding of molecular mechanisms and identify candidate genes and metabolic-signaling pathways in ruminants.

New and old criteria for diagnosing celiac disease: do they really differ? A retrospective observational study

BackgroundThe aim of this study is to compare two groups of celiac patients: the first one, in which diagnosis was based on a “biopsy sparing” approach according to the 2012 ESPGHAN criteria, and the second one, based on the biopsy approach like the one of the 1991 Revised Criteria, in order to find relevant difference for sex, M/F ratio, age at diagnosis, clinical features at the onset, presence and prevalence of concomitant autoimmune disorders.MethodsOur study involves 61 patients having the Celiac Disease (CD) onset from February 2013 to February 2020. The 32 patients who received diagnosis according “biopsy sparing” criteria were enrolled in group (1) The 29 patients who received diagnosis by duodenal biopsy were enrolled in group (2) Prevalence of comorbidities was analysed through chi-square test.ResultsIn group 1 the prevalence of comorbidities such as Insulin-Dependent Diabetes Mellitus (IDDM) and thyroiditis was of 53%, while in group 2 it was only of 24%. Analysing the IDDM prevalence between the two groups we found a relevant difference. At the same time, the prevalence of thyroiditis was also significantly different. In group 1, male patients, in particular, would seem to have a higher incidence of CD related autoimmune disorders.ConclusionsAn increased prevalence of IDDM, thyroiditis and juvenile idiopathic arthritis (JIA) in the first group would show that the “biopsy sparing” approach could expose patients to a greater length of disease activity that might be responsible for the onset of such comorbidities. Further studies should be carried out on more numerous samples of patients in order to confirm or not these data.

A comprehensive framework for the delimitation of species within the Bemisia tabaci cryptic complex, a global pest-species group.

Identifying cryptic species poses a substantial challenge to both biologists and naturalists due to morphological similarities. Bemisia tabaci is a cryptic species complex containing more than 44 putative species; several of which are currently among the world's most destructive crop pests. Interpreting and delimiting the evolution of this species complex has proved problematic. To develop a comprehensive framework for species delimitation and identification, we evaluated the performance of distinct data sources both individually and in combination among numerous samples of the B. tabaci species complex acquired worldwide. Distinct datasets include full mitogenomes, single-copy nuclear genes, restriction site-associated DNA sequencing, geographic range, host speciation, and reproductive compatibility datasets. Phylogenetically, our well-supported topologies generated from threedense molecular markers highlighted the evolutionary divergence of species of the B. tabaci complex and suggested that the nuclear markers serve as a more accurate representation of B. tabaci species diversity. Reproductive compatibility datasets facilitated the identification of at least 17 different cryptic species within our samples. Native geographic range information provides a complementary assessment of species recognition, while the host range datasets provide low rate of delimiting resolution. We further summarized different data performances in species classification when compared with reproductive compatibility, indicating that combination of mtCOI divergence, nuclear markers, geographic range provide a complementary assessment of species recognition. Finally, we represent a model for understanding and untangling the cryptic species complexes based on the evidence from this study and previously published articles.

Analysis of the Efficacy of Intense Pulsed Light Combined with Doxycycline in the Treatment of Rosacea

Objective: To investigate the clinical efficacy of intense pulsed light combined with doxycycline in the treatment of rosacea. Methods: A sample of 60 patients with rosacea admitted to our hospital from January 2021 to December 2023 were selected and grouped into the observation group (n = 30) and the control group (n = 30) by using the randomized numerical table sampling method. The patients in the control group were treated with doxycycline, and the patients in the observation group were treated with intense pulsed light combined with doxycycline. The clinical effective rate and recurrence rate, clinical symptom score, skin barrier function indexes, and the incidence of adverse reactions in the two groups were compared. Results: The clinical effective rate of the observation group was higher than that of the control group, and the recurrence rate was lower than that of the control group (P < 0.05); the clinical symptom score of the observation group was lower than that of the control group after treatment (P < 0.05); the water content of the stratum corneum of the observation group was higher than that of the control group after treatment, and the amount of transepidermal water loss was lower than that of the control group (P < 0.05); the incidence of adverse reactions of the two groups did not have any significant difference in comparison (P > 0.05). Conclusion: The treatment effect of intense pulsed light combined with doxycycline in rosacea patients is remarkable. It can alleviate clinical symptoms and improve the skin barrier function, with a lower recurrence rate after treatment and higher therapeutic safety, which is suitable for popularization and use in healthcare institutions.

A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes.

A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover, existing methods are not motivated by the strive for insights by means of an easy-to-interpret parameter. Hence, we seek to develop a new estimand-driven method to compare the effectiveness of two treatments in the context of right-censored survival data with matched pairs. With the help of competing risks techniques, the so-called relative treatment effect is estimated. This estimand describes the probability that individuals under Treatment1 have a longer lifetime than comparable individuals under Treatment2. We derive hypothesis tests and confidence intervals based on a studentized version of the estimator, where resampling-based inference is established by means of a randomization method. In a simulation study, we demonstrate for numerous sample sizes and different amounts of censoring that the developed test exhibits a good power. Finally, we apply the methodology to a well-known benchmark data set from a trial with patients suffering from diabetic retinopathy.

Heart Disease Prediction using Machine Learning

Machine Learning (ML) is increasingly applied in various sectors globally, and the healthcare sector is no exception. In particular, ML can significantly contribute to the early detection of locomotor disorders and heart diseases. Timely predictions can offer valuable insights to physicians, enabling them to tailor their diagnostic and treatment strategies for individual patients. This project focuses on the use of ML algorithms to predict the likelihood of heart disease in individuals. It involves a comparative analysis of several classifiers, including decision trees, Naïve Bayes, Logistic Regression, SVM, and Random Forest. Furthermore, the project introduces an ensemble classifier that combines the strengths of both robust and less robust classifiers. This approach allows for the utilization of numerous samples for training and validation purposes. We analyze both existing classifiers and proposed classifiers like AdaBoost and XGBoost, aiming to enhance accuracy and predictive capabilities.Heart disease remains a significant concern worldwide, and early detection is crucial for preventing severe outcomes and enhancing patient care. Machine learning techniques have shown promise in increasing the accuracy of heart disease predictions. This paper discusses the use of ML in predicting heart disease, emphasizing its potential advantages and the challenges encountered. The main goal of this research is to assess the performance of various ML algorithms in predicting heart disease risk based on patient data. The dataset comprises a wide array of variables, including age, gender, blood pressure, cholesterol levels, exercise patterns, and medical history. After preprocessing these variables, we train and test several ML models, such as Logistic Regression, Random Forest, and Support Vector Machines (SVM), to evaluate their effectiveness.

Accelerator mass spectrometry measurements of 233U in groundwater, soil and vegetation at a legacy radioactive waste site

Low-level radioactive wastes were disposed at the Little Forest Legacy Site (LFLS) near Sydney, Australia between 1960 and 1968. According to the disposal records, 233U contributes a significant portion of the inventory of actinide activity buried in the LFLS trenches. Although the presence of 233U in environmental samples from LFLS has been previously inferred from alpha-spectrometry measurements, it has been difficult to quantify because the 233U and 234U α-peaks are superimposed. Therefore, the amounts of 233U in groundwaters, soils and vegetation from the vicinity of the LFLS were measured using accelerator mass spectrometry (AMS). The AMS results show the presence of 233U in numerous environmental samples, particularly those obtained within, and in the immediate vicinity of, the trenched area. There is evidence for dispersion of 233U in groundwater (possibly mobilised by co-disposed organic liquids), and the data also suggest other sources of 233U contamination in addition to the trench wastes. These may include leakages and spills from waste drums as well as waste burnings, which also occurred at the site. The AMS results confirm the historic information regarding disposal of 233U in the LFLS trenches. The AMS technique has been valuable to ascertain the distribution and environmental behaviour of 233U at the LFLS and the results demonstrate the applicability of AMS for evaluating contamination of 233U at other radioactive waste sites.

Effect of Confining Pressure on the Macro- and Microscopic Mechanisms of Diorite under Triaxial Unloading Conditions

In this study, the response mechanism between macro- and microscales of deep hard-rock diorite is investigated under loading and unloading conditions. Moreover, the statistical theory is combined with particle flow code simulations to establish a correlation between unloading rates observed in laboratory experiments and numerical simulations. Subsequent numerical tests under varying confining pressures are conducted to examine the macroscopic mechanical properties and the evolution of particle velocity, displacement, contact force chain failures, and microcracks in both axial and radial directions of the numerical rock samples during the loading and unloading phases. The findings indicate that the confining pressure strength curve displays an instantaneous fluctuation response during unloading, which intensifies with higher initial confining pressures. This suggests that rock sample damage progresses in multiple stages of expansion and penetration. The study also reveals that with increased initial confining pressure, there is a decrease in particle velocity along the unloading direction and an increase in particle displacement and the number of contact force chain failures, indicating more severe radial expansion of the rock sample. Furthermore, microcracks predominantly accumulate near the unloading surface, and their total number escalates with rising confining pressure, suggesting that higher confining pressures promote the development and expansion of internal microcracks.

A novel biomarker assay qualification strategy for rare human matrices - a case study of biomarkers in aqueous humor.

Background: Measuring pharmacodynamic biomarkers near the therapeutic site of action presents considerable challenges for sites with limited matrix volume or difficult access. Bioanalytical method qualification requires theuse of numerous matrix samples, which is problematic for rare matrices. The aim of this study was to design and implement a streamlined, fit-for-purpose strategy for qualification of biomarker assays in rare matrices. Materials & methods: A multiplexed biomarker immunoassay was developed in human aqueous humor. Results: Our strategy was successfully implemented, providing characterization of assay performance while reducing number of samples in assay qualification. Our assay was used in clinical trial support for an ophthalmic drug candidate. Conclusion: Our results indicate this approach can be applied to other earlystage drug development programs facing similar challenges.

Strategies to deal with genetic analyzer-specific DNA methylation measurements.

Targeted bisulfite sequencing using single-base extension (SBE)can be used to measure DNA methylation via capillary electrophoresis on genetic analyzers in forensic labs. Several accurate age prediction models have been reported using this method. However, using different genetic analyzers with different software settings can generate different methylation values, leading to significant errors in age prediction. To address this issue, the study proposes and compares four methods as follows: (1) adjusting methylation values using numerous actual body fluid DNA samples, (2) adjusting methylation values using control DNAs with varying methylation ratios, (3) constructing new age prediction models for each genetic analyzer type, and (4) constructing new age prediction models that could be applied to all types of genetic analyzers. To test the methods for adjusting values using actual body fluid DNA samples, previously reported adjusting equations were used for blood/saliva DNA age prediction markers (ELOVL2, FHL2, KLF14, MIR29B2CHG/C1orf132, and TRIM59). New equations were generated for semen DNA age prediction markers (TTC7B, LOC401324/cg12837463, and LOC729960/NOX4) by drawing polynomial regression lines between the results of the three types of genetic analyzers (3130, 3500, and SeqStudio). The same method was applied to obtain adjustment equations using 11 control DNA samples. To develop new age prediction models for each genetic analyzer type, linear regression analysis was conducted using DNA methylation data from 150 blood, 150 saliva, and 62 semen samples. For the genetic analyzer-independent models, control DNAs were used to formulate equations for calibrating the bias of the data from each genetic analyzer, and linear regression analysis was performed using calibrated body fluid DNA data. In the comparison results, the genetic analyzer-specific models showed the highest accuracy. However, genetic analyzer-independent models through bias adjustment also provided accurate age prediction results, suggesting its use as an alternative in situations with multiple constraints.

Stratotypic section of the Wenlockian Voyvyv horizon (Western slope of the Subpolar Urals)

Research subject. The paper presents the results of a study into the carbonate-terrigenous section of 197.8 m-thick Wenlockian section in the Shchugor River basin, which is proposed as the stratotype of the regional horizon. Materials and methods. The study was based on numerous samples from Lower Silurian deposits collected by the authors in different years during fieldwork on the Western slope of the Circumpolar Urals and the Chernov and Chernyshev uplifts, as well as the biostratigraphic, paleontological, lithological, and C-isotopic data obtained during these works. Results. The obtained data allowed revising some of the established ideas about the structure of the Lower Silurian section and the position of the boundary level between the Llandoverian and Wenlockian in the Subpolar Urals and to identify a new regional subdivision with the name “Voyvyv”. The traced formation conditions of shallow carbonate sediments of the Voyvyv horizon and the consistent change of zonal taxa of marine fauna in the stratotype section indicate similarity with coeval strata on the Chernov and Chernyshev uplifts and Dolgy Island. Conclusions. The results of the study of Wenlockian Voyvyv deposits in the Timan-North Urals region showed that the section in the Shchugor River basin in outcrop 10c can be considered the most informative in the Northern Urals region in terms of exposure degree, established stratigraphic completeness, and preservation of macro- and microfauna.

Evaluation of Damage Stress Thresholds and Mechanical Properties of Granite: New Insights from Digital Image Correlation and GB-FDEM

We employed a novel combination of digital image correlation (DIC) and grain-based hybrid finite–discrete element method (GB-FDEM) to improve the comprehension of the relationships between microstructural features and the mechanical properties of granitic rocks. DIC and numerical results showed that macrocracks initiated and propagated along grain boundaries among different minerals driven by the high stiffness contrast between the compliant biotite and the stiffer feldspar/quartz grains. Surface deformation analyses revealed that tensile-dominated macrocracks open at monotonically increased rates before the crack damage threshold, and the opening accelerated afterwards with the increased shear component. The onset of the acceleration of the opening rate of macrocracks can be used to infer the crack damage threshold. Both strain and acoustic emission were used to infer damage stress thresholds in the synthetic numerical samples. Numerical results showed that the damage stress thresholds and uniaxial compressive strength decrease with increasing grain size following log-linear relations. Coarse-grained samples tend to fail by axial splitting, while fine-grained samples fail by shear zone formation. Biotite and quartz contents significantly affect mechanical properties, while quartz to feldspar ratio is positively related to the mechanical properties. Our study demonstrates the capacities of DIC and GB-FDEM in inferring damage conditions in granitic rocks and clarifies the microstructural control of the macroscopic mechanical behaviors. Our results also provide a comprehensive understanding of the systematics of strain localization, crack development, and acoustic emission during the rock progressive failure process.

Quality and composition of Lower Cretaceous carbonate source rocks and seepage oils in the Belize Basin, southern Belize, Central America

The Belize Basin in southern Belize, Central America, contains numerous seepage oils derived from marly and carbonate source rocks, but since no definitive source rocks have been identified the source remains uncertain. This study examines by pyrolysis and organic petrography the source rock quality and maturity of 66 samples collected from three newly exposed carbonate sections in the southeastern Belize Basin together with organic geochemical analysis of five new occurrences of seepage oils from the southern Belize Basin. Combined ammonite and aptychi biostratigraphy suggest a Berriasian to Hauterivian age of the source rock samples. Low Tmax values show the samples are thermally immature. Numerous samples are oil-prone and have Hydrogen Index values >300–400 mg HC/g TOC, reaching a maximum of 768 mg HC/g TOC. The samples with source rock potential contain from 32 to 64% pyrolysable organic carbon and contains liptinite-rich Type II kerogen consisting of alginite, bituminite, and liptodetrinite. The calculated Ultimate Expulsion Potential (UEP) of a 22.5 m thick section amounts to ∼2.5 mmboe/km2. This generation capacity is volumetrically relatively small, but it could be compensated for by a thicker source rock section. However, the identified source rocks could account for the seepage oils in the Belize Basin, and they are thus the first documentation of Lower Cretaceous source rocks in Belize. A plausible explanation for the widespread occurrence of seepage oils in the Belize Basin could be generation from organic-rich layers with bituminite and alginite. In line with this and previously examined seepage oils, the new variably degraded oils were sourced from carbonate source rock facies.

Grinding Surface Roughness Measurement Combined with Simulation Data and Transfer Learning

AbstractThe surface‐roughness machine vision measurement method requires training numerous samples with uniform roughness values to construct a prediction model. Insufficient actual sample size will make the model lack generalization ability. So, a grinding surface‐roughness measurement method combined with simulation data and transfer learning is proposed. First, through surface simulation, inverse fitting, and three‐dimensional (3D) modeling, more realistic simulation samples are obtained, real samples are prepared, and a simulation imaging system and an actual imaging experiment to acquire surface images are designed. Second, the data distribution difference between the simulation domain and the actual domain through transfer component analysis and the use of the adjusted simulation‐domain data and its label to train a support vector regression model is adjusted. Finally, the model predicts the surface roughness of the uncalibrated actual‐domain sample. For shared features of transferable simulation images and actual images, using the high sensitivity of color information, image features are enhanced and a proportion of pure color region index is designed, strongly correlated within the domain but less differentiated between domains. The results show that the transfer learning prediction accuracy based on this index exceeds 90%, verifying the feasibility of the proposed method and providing a new improvement strategy for sample expansion in visual roughness measurement.

Global Mapping of Three-Dimensional (3D) Urban Structures Reveals Escalating Utilization in the Vertical Dimension and Pronounced Building Space Inequality

Three-dimensional (3D) urban structures play a critical role in informing climate mitigation strategies aimed at the built environment and facilitating sustainable urban development. Regrettably, there exists a significant gap in detailed and consistent data on 3D building space structures with global coverage due to the challenges inherent in the data collection and model calibration processes. In this study, we constructed a global urban structure dataset (GUS-3D), including building volume, height, and footprint information, at a 500 m spatial resolution using extensive satellite observation products and numerous reference building samples. Our analysis indicated that the total volume of buildings worldwide in 2015 exceeded 1 × 1012 m3. Over the 1985 to 2015 period, we observed a slight increase in the magnitude of 3D building volume growth (i.e., it increased from 166.02 km3 during the 1985–2000 period to 175.08 km3 during the 2000–2015 period), while the expansion magnitudes of the two-dimensional (2D) building footprint (22.51 × 103 km2 vs. 13.29 × 103 km2) and urban extent (157 × 103 km2 vs. 133.8 × 103 km2) notably decreased. This trend highlights the significant increase in intensive vertical utilization of urban land. Furthermore, we identified significant heterogeneity in building space provision and inequality across cities worldwide. This inequality is particularly pronounced in many populous Asian cities, which has been overlooked in previous studies on economic inequality. The GUS-3D dataset shows great potential to deepen our understanding of the urban environment and creates new horizons for numerous 3D urban studies.

Using capillary electrophoresis to identify Anopheline species in routine sampling sites.

In the Anopheles genus, various mosquito species are able to transmit the Plasmodium parasites responsible for malaria, while others are non-vectors. In an effort to better understand the biology of Anopheles species and to quantify transmission risk in an area, the identification of mosquito species collected in the field is an essential but problematic task. Morphological identification requires expertise and cannot be checked after processing samples in a destructive treatment, while sequencing of numerous samples is costly. Here, we introduce a method of Species identification via Simple Observation Coupled with Capillary Electrophoresis Technology (SOCCET). This molecular technique of species identification is based on precise determination of ITS2 length combined with a simple visual observation, the colour of mosquito hindleg tip. DNA extracted from field-collected Anopheles mosquitoes was amplified with universal Anopheles ITS2 primers and analysed with a capillary electrophoresis device, which precisely determines the size of the fragments. We defined windows of amplicon sizes combined with fifth hind tarsus colour, which allows discrimination of the major Anopheles species found in our collections. We validated our parameters via Sanger sequencing of ITS2 amplicons. Using the SOCCET method, we characterised the composition of Anopheles populations in five locations of French Guiana, where we detected a total of nine species. Anopheles braziliensis and Anopheles darlingi were detected in four locations each and represented 13 and 67% of our samples, respectively. The SOCCET method can be particularly useful when working with routine sampling sites with a moderate species diversity, that is, when the number of local species is too high to define species-specific primers but low enough to avoid individual ITS2 sequencing. This tool will be of interest to evaluate local malaria transmission risk and this approach may be further implemented for other mosquito genera.

An numerical investigation of the three dimensional multi-level force chain network of the sample with a single fissure under uniaxial compression

A novel three-dimensional grain-based model based on Particle Flow Code is proposed. Within this model, both quantitative calculations and multi-level classifications of force chain networks are achieved. A batch of numerical samples with a single pre-existing fissure with different inclination angle αs is constructed and then applied to the uniaxial compression. The result shows that the general force chains (GF) are approximately uniformly distributed in each orientation. The number of high-strength force chains (HF) reaches its maximum at the peak load moment, and the main orientation is consistent with the loading direction. The average value of the force chain in the tip regions of the fissure at any characteristic moment is the highest. In the whole region and the upper and lower regions of the fissure, the level of the force chain network rises as αs increases. Conversely, within the tip regions of the fissure, both the average value and sum value of GF decrease. The fracture resistance of intragranular structures is the highest, surpassing that of both all structures and intergranular structures. The fracture resistance within the tip regions of the fissure is the lowest compared to the entire region and the upper and lower regions of the fissure.

Efficient super-resolution image reconstruction of electrical capacitance tomography for gas–solid fluidized bed measurement

Electrical capacitance tomography (ECT) has been widely used to investigate the flow dynamics of the gas–solid fluidized bed. Deep learning-based tomographic imaging of ECT is of great potential to reconstruct the particle concentration distribution with compatible image quality and reconstruction speed. However, it suffers from low model construction efficiency as numerous data samples need to be collected for training the deep neural network. Here a super-resolution (SR) imaging model based on a two-stage imaging network is proposed to improve the model construction efficiency. A low-resolution (LR) imaging network is used to reconstruct the LR image with the measured capacitance matrix while a subsequent high-resolution (HR) network calculates the HR image with the reconstructed LR image. The imaging performance, construction efficiency, and generalization ability of the trained SR imaging model are evaluated by reconstructing the samples in the test set and the unseen typical flow patterns. Considering the training set includes 100,000 data samples, the time consumption of numerically collecting the dataset for training the SR imaging model is 2.70 h, 2.2 % of that for a conventional deep learning-based imaging model of ECT, and the overall model construction time is only 4.44 h when the grid numbers of the LR and HR images are 246 and 8053, respectively. Experimental measurements are further carried out on a fluidized bed and the preliminary results show good prediction and generality of the trained model for reconstructing the particle concentration distribution.