Prior Extraction Research Articles

A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning

Annotation and quantification of cardiovascular in intravascular ultrasound (IVUS) images are of great significance in assisting the clinical diagnosis and treatment of coronary heart disease. However, it is time-consuming and expensive for cardiologists to manually annotate cardiovascular and calculate parameters in IVUS images using different software during the diagnosis. In this paper, we propose an IVUS analysis toolbox (SaCAQ) for semi-automatic cardiovascular annotation and quantification via prior knowledge-guided feature learning. The SaCAQ consists of two main modules: (1) a semi-automatic cardiovascular annotation module and (2) a cardiovascular quantification module. The semi-automatic cardiovascular annotation module can accurately segment the vascular wall by a IVUS images segmentation model (ISM-IVUS). The ISM-IVUS first extracts vascular morphological features and pixel intensity change features for the complex image segmentation by the expert prior knowledge feature extraction mechanism. Secondly, vascular wall is accurately segmented by the multi-scale feature extraction of attention U-Net. Moreover, the segmentation result checking mechanism is applied to verify the automatic segmentation result, which enhances the reliability of SaCAQ. Plaque regions of interest to doctors are detected by the rule-based plaque detection method, which can assist in the quantification task. Finally, image measurement methods and medically recognized calculation formulas are used in the cardiovascular quantification module to obtain clinical parameters, which significantly improves the confidence of the results. The SaCAQ is verified on our own dataset containing 5242 IVUS slice images and 383 sets of parameters. The experiments demonstrate that the SaCAQ achieves high accuracy of vascular wall segmentation: mIoU of 86.16%, Dice coefficient of 93.43%, and Hausdorff distance of 0.2171 mm. Moreover, ISM-IVUS has good segmentation speed and robustness. The calculated parameters show good correlation and p-values. All of these results indicate that the proposed SaCAQ provides an efficient, accurate, and reliable toolbox for clinical diagnosis of cardiovascular disease. The SaCAQ and user manual can be publicly available from https://github.com/zHwiz/SaCAQ.

Retinal structure guidance-and-adaption network for early Parkinson’s disease recognition based on OCT images

Parkinson’s disease (PD) is a leading neurodegenerative disease globally. Precise and objective PD diagnosis is significant for early intervention and treatment. Recent studies have shown significant correlations between retinal structure information and PD based on optical coherence tomography (OCT) images, providing another potential means for early PD recognition. However, how to exploit the retinal structure information (e.g., thickness and mean intensity) from different retinal layers to improve PD recognition performance has not been studied before. Motivated by the above observations, we first propose a structural prior knowledge extraction (SPKE) module to obtain the retinal structure feature maps; then, we develop a structure-guided-and-adaption attention (SGDA) module to fully leverage the potential of different retinal layers based on the extracted retinal structure feature maps. By embedding SPKE and SGDA modules at the low stage of deep neural networks (DNNs), a retinal structure-guided-and-adaption network (RSGA-Net) is constructed for early PD recognition based on OCT images. The extensive experiments on a clinical OCT-PD dataset demonstrate the superiority of RSGA-Net over state-of-the-art methods. Additionally, we provide a visual analysis to explain how retinal structure information affects the decision-making process of DNNs.

Multidimensional Directionality-Enhanced Segmentation via large vision model

Optical Coherence Tomography (OCT) facilitates a comprehensive examination of macular edema and associated lesions. Manual delineation of retinal fluid is labor-intensive and error-prone, necessitating an automated diagnostic and therapeutic planning mechanism. Conventional supervised learning models are hindered by dataset limitations, while Transformer-based large vision models exhibit challenges in medical image segmentation, particularly in detecting small, subtle lesions in OCT images. This paper introduces the Multidimensional Directionality-Enhanced Retinal Fluid Segmentation framework (MD-DERFS), which reduces the limitations inherent in conventional supervised models by adapting a transformer-based large vision model for macular edema segmentation. The proposed MD-DERFS introduces a Multi-Dimensional Feature Re-Encoder Unit (MFU) to augment the model’s proficiency in recognizing specific textures and pathological features through directional prior extraction and an Edema Texture Mapping Unit (ETMU), a Cross-scale Directional Insight Network (CDIN) furnishes a holistic perspective spanning local to global details, mitigating the large vision model’s deficiencies in capturing localized feature information. Additionally, the framework is augmented by a Harmonic Minutiae Segmentation Equilibrium loss (LHMSE) that can address the challenges of data imbalance and annotation scarcity in macular edema datasets. Empirical validation on the MacuScan-8k dataset shows that MD-DERFS surpasses existing segmentation methodologies, demonstrating its efficacy in adapting large vision models for boundary-sensitive medical imaging tasks. The code is publicly available at https://github.com/IMOP-lab/MD-DERFS-Pytorch.git.

An integrated paradigm between green analytical chemistry and quality-by-design for robust analysis of alcaftadine and ketorolac tromethamine in aqueous humor via sustainable chromatographic methods

The field of pharmaceutical industry has witnessed the launch of a new co-formulated eye drop, composed of alcaftadine and ketorolac tromethamine, for alleviating allergic conjunctivitis. Such new formulation evokes the need for a sensitive, robust and fast analytical method capable of their quantification in their dosage form, in aqueous humor for pharmaco-kinetics studies as well as in presence of plausible degradation products. In this work, two validated stability-indicating chromatographic methods were developed for alcaftadine and ketorolac assaying with good adherence to values of green analytical chemistry. Drugs stabilities were tested under various stress conditions where drugs showed liability for oxidative degradation only. For the first chromatographic method, an integrated merger between analytical quality-by-design and green analytical chemistry was implemented via a UPLC method coupled with photo-diode array detector. A short C18 analytical column of 100 mm was utilized with mobile phase of ethanol: aqueous solution pH 4.0 containing 0.1% triethylamine (40: 60, by volume). Second method was thin layer chromatographic one using HPTLC silica plates as a stationary phase and a developing system of dichloromethane: ethanol (5:4, by volume). Both methods achieved to selectively quantify the studied drugs with high accuracy and precision without any interference from degradation products or related excipients in eye drops. The methods were also successfully exploited for ALF and KTC assaying in rabbit aqueous humor, with no need for prior extraction procedures, for further pharmacokinetic studies. Finally, usage of green solvents gives us the advantage of applying various evaluation tools to conclude method greenness.

DeepVID v2: self-supervised denoising with decoupled spatiotemporal enhancement for low-photon voltage imaging.

Voltage imaging is a powerful tool for studying the dynamics of neuronal activities in the brain. However, voltage imaging data are fundamentally corrupted by severe Poisson noise in the low-photon regime, which hinders the accurate extraction of neuronal activities. Self-supervised deep learning denoising methods have shown great potential in addressing the challenges in low-photon voltage imaging without the need for ground-truth but usually suffer from the trade-off between spatial and temporal performances. We present DeepVID v2, a self-supervised denoising framework with decoupled spatial and temporal enhancement capability to significantly augment low-photon voltage imaging. DeepVID v2 is built on our original DeepVID framework, which performs frame-based denoising by utilizing a sequence of frames around the central frame targeted for denoising to leverage temporal information and ensure consistency. Similar to DeepVID, the network further integrates multiple blind pixels in the central frame to enrich the learning of local spatial information. In addition, DeepVID v2 introduces a new spatial prior extraction branch to capture fine structural details to learn high spatial resolution information. Two variants of DeepVID v2 are introduced to meet specific denoising needs: an online version tailored for real-time inference with a limited number of frames and an offline version designed to leverage the full dataset, achieving optimal temporal and spatial performances. We demonstrate that DeepVID v2 is able to overcome the trade-off between spatial and temporal performances and achieve superior denoising capability in resolving both high-resolution spatial structures and rapid temporal neuronal activities. We further show that DeepVID v2 can generalize to different imaging conditions, including time-series measurements with various signal-to-noise ratios and extreme low-photon conditions. Our results underscore DeepVID v2 as a promising tool for enhancing voltage imaging. This framework has the potential to generalize to other low-photon imaging modalities and greatly facilitate the study of neuronal activities in the brain.

In Vitro Antioxidant and Antifungal Activities of Extracts from Ocimum basilicum Leaves Validated by Molecular Docking and ADMET Analysis.

The aim of study is to investigate the various mechanism-based antioxidant and anti-fungal properties of a hydroalcoholic extract of Ocimum basilicum L leaves. Additionally, conduct molecular docking to simultaneously validate in vitro activities. Also, perform ADMET analysis to know pharmacokinetic properties and its toxicity for its safety. Prior extract's qualitative analysis has been performed to identify the bioactive compounds by phytochemical tests and GC-MS analysis. Different mechanism-based in vitro antioxidant methods are studied; in different methods, different IC50 values have come, which revealed the extract's antioxidant potentials. The antifungal potential of the extract has been observed by performing a modified poison food assay and a time-killing curve assay. In silico analysis with the human peroxiredoxin 5 enzyme (PDB ID: 1HD2) and secreted aspartic proteinase (PDB ID: 2QZX), which predict extract biological activity, has shown promising results of Ocimum basilicum L extract. In silico findings confirm the in vitro experimental outcome of the extract. The different IC50 values of extracts in different mechanisms indicate their therapeutic potential, and that encourages further research in formulation development. The time-killing assay method gives information about a dynamic interaction between extract and microbial strain. Concentration-dependent antifungal studies have significance in formulation development for dose determination.

Assessing the predictability of five intraocular lens calculation methods in eyes with prior myopic keratorefractive lenticule extraction.

To evaluate and compare the predictability of five methods of intraocular lens (IOL) calculation in eyes with prior keratorefractive lenticule extraction (KLEx) for the treatment of myopia. A retrospective case study included 100 eyes of 52 patients who underwent myopia and myopia with astigmatism treatment with small incision lenticule extraction (SMILE). Preoperative and 3-month postoperative measurements of optical biometry and corneal tomography were obtained. The spherical equivalent of the refractive change induced by surgery was converted to the corneal plane (SMILE-dif). A physically well-defined method was developed in which the same IOL model was implanted before and after SMILE. IOL power was calculated using ray-tracing (RT-Sirius), and several IOL power calculation formulas (Kane, EVO 2.0, Barrett Universal II Formula, Hoffer QST) before surgery. After surgery, IOL power was calculated with RT-Sirius, Kane using Mean Pupil Power at 5.5mm by ray tracing, EVO 2.0 Post Myopic LASIK/PRK, Barrett True K and Hoffer QST Post Myopic LASIK/PRK after surgery. The difference between the refractive error induced by the IOL before and after SMILE in the corneal plane (IOL-dif) was compared with SMILE-dif. The predicted error (PE) was calculated as the difference between SMILE-dif and IOL-dif. The PE obtained was 0.26 ± 0.55 diopters (D), 0.10 ± 0.45 D, 0.40 ± 0.37 D, -0.03 ± 0.36 D, 0.02 ± 0.51 D, with RT-Sirius, Kane, EVO 2.0, Barrett True K, and Hoffer QST respectively. PE was not statistically significantly different between Barrett True K and Hoffer QST, with differences being more homogeneous with Barrett, (variance σ2 = 0,13). The absolute EP obtained with Barrett True K achieved 84% of cases within ± 0.5 D, followed by Kane (72%), Hoffer QST (65%), EVO (61%) and RT-Sirius (59%). Barrett True K formula was the most accurate method for IOL calculation in eyes that had undergone SMILE for the correction of myopia. What is known The literature regarding IOL power calculation after SMILE is sparse, and the methods used to estimate corneal power following LASIK/PRK may not be applicable to SMILE procedures. The most common approach to investigating the predictability of IOL calculation formulas involves a theoretical model encompassing the virtual implantation of an IOL. What is new The Hoffer QST formula, Kane formula using Mean Pupil Power at 5.5mm, EVO 2.0, and Sirius' Ray Tracing software had not been previously evaluated using this approach. The Barrett True K formula was the most accurate method for IOL calculation in eyes that had undergone SMILE for myopia correction, outperforming Ray Tracing.

Computationally Optimized Graphene-Based Electrochemical Sensor with Enhanced Signal Stability for the Determination of the Antimicrobial Agent 9-aminoacridine

A hydrophobic aryl diazonium salt has been synthesized from 3,5-bis(trifluoromethyl)aniline and utilized to covalently modify graphene nanoplatelets and carbon nanotubes. The modified nanomaterials were applied on a screen-printed electrode/ion sensing membrane interface resulting in reduced potential drift to 100 μV h−1 compared to control sensors. Characterization was achieved through X-ray photoelectron spectroscopy. The electrode’s response was optimized using response surface methodology and then utilized for determination of 9-Aminoacridine (9-AA) in pharmaceutical gel dosage form and spiked human plasma without prior extraction steps. 9-AA is a fluorescent dye with antimicrobial activity that eradicates a range of microorganisms that can cause oral sores or broken skin and it has been recently used as anticancer among other uses as fluorescent dye and pH indicator. Accurate determination of 9-AA could help in adjusting dosages for each application. The optimized sensor was validated per IUPAC guidelines and obtained a wide linearity range from 1.0 × 10–7 M to 1.0 × 10–2 M, correlation coefficient of 0.9997, improved Nernstian slope 59.72, long term stability, and lower limit of detection (9.0 × 10–8 M). Furthermore, Analytical Eco-scale and AGREE methods were utilized to evaluate the presented method’s greenness.

Reliable multi-modal medical image-to-image translation independent of pixel-wise aligned data.

The current mainstream multi-modal medical image-to-image translation methods face a contradiction. Supervised methods with outstanding performance rely on pixel-wise aligned training data to constrain the model optimization. However, obtaining pixel-wise aligned multi-modal medical image datasets is challenging. Unsupervised methods can be trained without paired data, but their reliability cannot be guaranteed. At present, there is no ideal multi-modal medical image-to-image translation method that can generate reliable translation results without the need for pixel-wise aligned data. This work aims to develop a novel medical image-to-image translation model that is independent of pixel-wise aligned data (MITIA), enabling reliable multi-modal medical image-to-image translation under the condition of misaligned trainingdata. The proposed MITIA model utilizes a prior extraction network composed of a multi-modal medical image registration module and a multi-modal misalignment error detection module to extract pixel-level prior information from training data with misalignment errors to the largest extent. The extracted prior information is then used to construct a regularization term to constrain the optimization of the unsupervised cycle-consistent Generative Adversarial Network model, restricting its solution space and thereby improving the performance and reliability of the generator. We trained the MITIA model using six datasets containing different misalignment errors and two well-aligned datasets. Subsequently, we conducted quantitative analysis using peak signal-to-noise ratio and structural similarity as metrics. Moreover, we compared the proposed method with six other state-of-the-art image-to-image translationmethods. The results of both quantitative analysis and qualitative visual inspection indicate that MITIA achieves superior performance compared to the competing state-of-the-art methods, both on misaligned data and aligned data. Furthermore, MITIA shows more stability in the presence of misalignment errors in the training data, regardless of their severity or type. The proposed method achieves outstanding performance in multi-modal medical image-to-image translation tasks without aligned training data. Due to the difficulty in obtaining pixel-wise aligned data for medical image translation tasks, MITIA is expected to generate significant application value in this scenario compared to existingmethods.

A suggested eco-friendly procedure for the recovery of uranium from Egyptian Abu Tartur phosphorites before industrial processing

The manufacture of phosphate fertilizers without prior uranium extraction leads to widespread dispersal of uranium compounds across agricultural fields, posing significant cumulative environmental and health risks. Furthermore, this represents a wasteful loss of a critical global energy resource. Against this backdrop, we propose an environmentally friendly method for extracting uranium from Egyptian Abu Tartur phosphate rocks using humic acid (HA). Almost complete dissolution of uranium with a 3% HA concentration, solid-to-liquid ratio of 1/2, and a leaching temperature of 60°C. The uranium was then effectively adsorbed from the HA solution, with a concentration of 0.026 g/L, onto activated carbon, influenced by variables such as: pH, contact time, and adsorbent amount per leach solution volume. Subsequent uranium regeneration from the activated carbon was accomplished using 0.5 M ammonium bicarbonate, achieving a desorption efficiency of 98.7%. This process culminated in the production of a sodium diuranate concentrate following uranium precipitation with NaOH solution.

Novel avenue of carotenoids & omega-3 rich carrot waste extract in innovative table spread: optimization (based on texture and sensory) and characterization

Carrot waste is a rich source of pro-vitamin A, β-carotene, recognized for its immunomodulatory and health-promoting properties. The present study was aimed at formulating an innovative table spread (ITS) enriched with concentrated carotenoid extract derived from carrot pomace. Prior extraction of carotenoids in flaxseed oil was optimized resulting in extract (CE) rich in carotenoids (82.66 µg/g) and alpha-linolenic acid (ALA). The effects of three ingredients i.e. CE, tri-sodium citrate (TC), and common salt (CS) on ITS formulated using 33 factorial experiment (27 treatment combinations) were determined employing three-way ANOVA of Proc GLM method of SAS 9.3 for its textural and sensory acceptability. The optimized formulation contained 15% CE, 1.0% CS and 0.3% TC. ITS displayed medium (≈51% fat) internal phase emulsion (confirmed via fluorescence microscopy) stabilized (-56.867 mV ± 0.850 zeta potential) by whey protein concentrate and possessed high thermal stability (studied through thermogravimetric analysis). The control table spread (CTS) was prepared using all ingredients except CE replaced with flaxseed oil. ITS showed significantly (p < 0.05) higher carotenoid, antioxidant (ABTS, DPPH and FRAP), yellowness index and b* value than CTS; while non-significant (p > 0.05) difference existed for proximate composition and acidity. ITS turned out to be a naturally colored functional product and excellent source of carotenoids (24.276 μg/g ± 0.076) and ALA (13.816 g/ 100 g fat).

DGA-grafting pyridine for ultra-selective and prior extraction of 99TcO4− from simulated spent nuclear fuel

Selective and prior extraction of 99TcO4− ahead of uranium and plutonium separation is a beneficial strategy for the modern nuclear fuel cycle. Herein, a novel DGA-grafting pyridine ligand BisDODGA-DAPy (L1) was tailored for the efficient separation of TcO4− from simulated spent nuclear fuel based on the selectivity of pyridine and synergistic effect of diglycolamide (DGA) group. Compared to the ligands BisDOSCA-DAPy (L2) and BisDODGA-MPDA (L3) with similar structure, BisDODGA-DAPy (L1) demonstrated the better separation performance including good extraction efficiency, reusability, and high loading capacity for TcO4− under high acidic medium. The interactions of the ligands with Tc(VII)/Re(VII) have been investigated in detail using FT-IR, 1H NMR titration, UV-Vis spectrophotometric titration, ESI-HRMS and DFT simulations. The extraction mechanism affected by the protonation of ligand was elucidated under different acidity. BisDODGA-DAPy (L1) demonstrated the ultra-selective extraction ability for TcO4− from simulated spent nuclear fuel. The maximum SFTc/U and SFTc/Pu values were up to 1.29 × 104 and 5.08 × 103, respectively. In the presence of 9 × 104-fold excess of NO3−, the extraction of TcO4− was almost unaffected. Moreover, the good radiolytic stability further highlights the promising potential of this ligand for 99Tc separation. DFT calculation revealed the dominant role of DAPy and DODGA in TcO4− extraction, providing the theoretical evidence for BisDODGA-DAPy (L1) to selectively bind TcO4− over NO3−.

Development and Optimization of the Extrusion of Potato Peel–Based Biocomposite Films

ABSTRACTSide streams from the agricultural and food industries are considered an interesting alternative source for the production of packaging materials due to their composition. However, their use often involves complex and energy‐intensive extraction of individual components. Therefore, this study investigated the application of entire potato peels, without prior extraction, to produce extruded films. Potato peels (PP) and biobased polybutylene succinate adipate (BioPBS) at 1:0, 3:2, 1:1 and 2:3 ratios were used to produce pellets and films in a two‐stage extrusion process. The influence of PP concentration, the addition of maleic anhydride and tartaric acid as compatibilizers and the addition of carnauba wax on the film properties were characterized in terms of morphological, mechanical, physicochemical and thermal properties. Films with a high PP content (50%–100%) showed weak mechanical properties. However, increasing the BioPBS content to PP/PBS 2:3 resulted in a significant increase in tensile strength and elongation at break to 6.20 MPa and 38.09%, respectively, and significantly reduced the water vapour permeability of the film to 5.5 × 10−6 gm/m2 d Pa. The use of 1% maleic anhydride had no significant effect on tensile strength (6.23 MPa), but further increased the elasticity to 65.48%, with no additional effect on barrier properties. The addition of 0.5% tartaric acid showed a similar elongation at break (56.23%) while significantly increasing the tensile strength to 6.56 MPa. The incorporation of 5% carnauba wax increased the surface hydrophobicity of the films from 69.5° to 99.7°, but at the expense of a decrease in mechanical properties.

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.Graphical

Exploring evolutionary-tuned autoencoder-based architectures for fault diagnosis in a wind turbine gearbox

ABSTRACT Vibration-based fault diagnosis from rotary machinery requires prior feature extraction, feature selection, or dimensionality reduction. Feature extraction is tedious, and computationally expensive. Feature selection presents unique challenges intrinsic to the method adopted. Nonlinear dimensionality reduction may be achieved through kernel transformations, however there is often a trade-off in information to achieve this. Given the above, this study proposes a novel autoencoder (AE) pre-processing framework for vibration-based fault diagnosis in wind turbine (WT) gearboxes. In this study, AEs are used to learn the features of WT gearbox vibration data while simultaneously compressing the data, obviating the need for costly feature engineering and dimensionality reduction. The effectiveness of the proposed framework was evaluated by training genetically optimized linear discriminant analysis (LDA), multilayer perceptron (MLP), and random forest (RF) models, with the AE’s latent space features. The models were evaluated using known classification metrics. The results showed that the performance of the models depends on the size of the AE’s latent space. As the size of the AE’s latent space increased, the quality of features extracted improved until a plateau was observed at a latent space dimension of 10. The AE pre-processed genetically optimized RF, MLP, and LDA models, designated AE-Pre-GO-RF, AE-Pre-GO-MLP, and AE-Pre-GO-LDA, were evaluated for accuracy, sensitivity, and specificity in the classification of seven (7) gearbox fault conditions. The AE-Pre-GO-RF model outperformed its counterparts, scoring 100% for all evaluated metrics, though with the longest training time (239.50 sec). Comparable results were found comparing this study with similar investigations involving traditional vibration processing techniques. More so, it was established that effective fault diagnosis of the WT gearbox can be achieved through manifold learning with AEs without expensive feature engineering.

Innovative application of Nile Red (NR)-based dye for direct detection of micro and nanoplastics (MNPs) in diverse aquatic environments

This paper presents the results of a research aimed at establishing a novel method for the detection of primary and secondary micro- and nanoplastics (MNPs), by using the fluorescence properties of the dye Nile Red-n-heptane (NR-H). The method has been applied to the detection of laboratory degraded polymers (Polystyrene, PS and Polyethylene Terephthalate, PET) as well as traceable latex microspheres in aqueous environments, showing a remarkable detection capacity and avoiding the prior extraction or processing of MNPs in natural samples, with significant time savings compared to conventional methods. The study has been carried out on various types of water, including samples from wastewater treatment plants, boreholes, seawater and synthesized seawater. The effectiveness of the staining process was evaluated by scanning electron microscopy (SEM), dynamic light scattering (DLS) and optical microscopy. As a result, a novel standardizable protocol for the rapid detection of MNPs has been established, with the potential to improve environmental protection through fast in-situ detection and identification of plastic contaminants. The limitations of the protocol in the quantification of MNPs have also been identified and further studies are proposed to overcome these limitations.

Ultrasound-based jawbone surface quality evaluation after alveolar ridge preservation.

Bone readiness for implant placement is typically evaluated by bone quality/density on 2-dimensional radiographs and cone beam computed tomography at an arbitrary time between 3 and 6months after tooth extraction and alveolar ridge preservation (ARP). The aim of this study is to investigate if high-frequency ultrasound (US) can classify bone readiness in humans, using micro-CT as a reference standard to obtain bone mineral density (BMD) and bone volume fraction (BVTV) of healed sockets receiving ARP in humans. A total of 27 bone cores were harvested during the implant surgery from 24 patients who received prior extraction with ARP. US images were taken immediately before the implant surgery at a site co-registered with the tissue biopsy collection location, made possible with a specially designed guide, and then classified into 3 tiers using B-mode image criteria (1) favorable, (2) questionable, and (3) unfavorable.Bone mineral density (hydroxyapatite) and BVTV were obtained from micro-CT as the gold standard. Hydroxyapatite and BVTV were evaluated within the projected US slice plane and thresholded to favorable (>2200mg/cm3; >0.45mm3/mm3), questionable (1500-2200mg/cm3; 0.4-0.45mm3/mm3), and unfavorable (<1500mg/cm3; <0.4mm3/mm3). The present US B-mode classification inversely scales with BMD. Regression analysis showed a significant relation between US classification and BMD as well as BVTV. T-test analysis demonstrated a significant correlation between US reader scores and the gold standard. When comparing Tier 1 with the combination of Tier 2 and 3, US achieved a significant group differentiation relative to mean BMD (p=0.004, true positive 66.7%, false positive 0%, true negative 100%, false negative 33.3%, specificity 100%, sensitivity 66.7%, receiver operating characteristics area under the curve 0.86). Similar results were found between US-derived tiers and BVTV. Preliminary data suggest US could classify jawbone surface quality that correlates with BMD/BVTV and serve as the basis for future development of US-based socket healing evaluation after ARP.

Calcium isotopic composition of the bulk silicate Earth: A komatiite perspective

Calcium (Ca) isotopic composition of the mantle has been previously estimated as δ44/40Ca. However, δ44/40Ca of the mantle records both its stable isotopic composition and excess/deficit in radiogenic 40Ca that can be produced by the decay of 40K. Here, we present the high-precision Ca isotopic compositions of 16 Archean komatiites from five representative localities (Barberton, Munro, Abitibi, Yilgarn, and Taishan). Due to the high degrees of partial melting that led to their formation, these komatiites are used here to simultaneously estimate ε40/44Ca, δ44/40Ca, and δ44/42Ca of the bulk silicate Earth. The komatiites exhibit a resolvable negative ε40/44Ca relative to SRM915a, with an average of −0.6 ± 0.1 (2SE, 95 c.i.% if not specified). Given the lack of significant differences among the samples and the non-zero value of ε40/44Ca, we suggest using δ44/42Ca to report the stable Ca isotopic composition in this study. Excluding one sample (SD6/400), which may have been affected by alteration, the remaining komatiites display relatively homogeneous δ44/42Ca, ranging from 0.39 ± 0.02‰ (2SE, N = 16) to 0.49 ± 0.02‰ (2SE, N = 10), defining a mean of 0.44 ± 0.02‰ (2SE, N = 15). The absence of correlations between δ44/42Ca and LOI, Sr/Nd, (Dy/Yb)N, and (La/Sm)N among the komatiites indicates that post-eruption alteration, the influence of residual garnet, as well as prior melt extraction from the mantle source have had a negligible influence on their stable Ca isotopic compositions. Based on available experimental petrological data and isotope fractionation factors, it is demonstrated that komatiites were insignificantly fractionated from their mantle sources (i.e., <0.02% in δ44/42Ca). Their δ44/42Ca represents the ambient mantle at the time of segregation. Accordingly, we suggest that the average δ44/42Ca and ε40/44Ca of the komatiites measured here are representative of the bulk silicate Earth. Our new estimate can serve as a reference point for Ca isotopes as a two-dimensional tracer for sources and geological processes.

Effects of renormalon scheme and perturbative scale choices on determinations of the strong coupling from e+e− event shapes

We study the role of renormalon cancellation schemes and perturbative scale choices in extractions of the strong coupling constant αs(mZ) and the leading nonperturbative shift parameter Ω1 from resummed predictions of the e+e− event shape thrust. We calculate the thrust distribution to NL3L′ resummed accuracy in soft-collinear effective theory (SCET) matched to the fixed-order O(αs2) prediction, and perform a new high-statistics computation of the O(αs3) matching in , although we do not include the latter in our final αs fits due to some observed systematics that require further investigation. We are primarily interested in testing the phenomenological impact sourced from varying amongst three renormalon cancellation schemes and two sets of perturbative scale profile choices. We then perform a global fit to available data spanning center-of-mass energies between 35–207 GeV in each scenario. Relevant subsets of our results are consistent with prior SCET-based extractions of αs(mZ), but we are also led to a number of novel observations. Notably, we find that the combined effect of altering the renormalon cancellation scheme and profile parameters can lead to few-percent-level impacts on the extracted values in the αs−Ω1 plane, indicating a potentially important systematic theory uncertainty that should be accounted for. We also observe that fits performed over windows dominated by dijet events are typically of a higher quality than those that extend into the far tails of the distributions, possibly motivating future fits focused more heavily in this region. Finally, we discuss how different estimates of the three-loop soft matching coefficient cS˜3 can also lead to measurable changes in the fitted {αs,Ω1} values. Published by the American Physical Society 2024

Intelligent algorithm based on deep learning to predict the dosage for anesthesia: A study on prediction of drug efficacy based on deep learning.

Anesthetic drugs play a vital role during surgery, however, due to individual differences and complex physiological mechanisms, the prediction of anesthetic drug dosage has always been a challenging problem. In this study, we propose a model for predicting the dosage of anesthetic drugs based on deep learning to help anesthesiologists better control their dosage during surgical procedures. We design a model based on the artificial neural network to predict the dosage of preoperative anesthetic, and use the SELU activation function and the loss function for weighted regularization to solve the problem of unbalanced sample. Moreover, we design a CNN-based model for the prior extraction of intraoperative features by using a 7 × 1 convolution kernel to enhance the receptive field, and combine maximum pooling and average pooling to extract key features while eliminating noise. A predictive model based on the LSTM network is designed to predict the intraoperative dosage of the anesthetic, and the bidirectional propagation-based LSTM network is used to improve the ability to learn the trend of changes in the physiological states of the patient during surgery. An attention module is added before the connection layer to appropriately attend to areas containing prominent features. The results of experiments showed that the proposed method reduced values of the MAPE to 15.83% and 12.25% compared with the traditional method in predictions of the preoperative and intraoperative doses of the anesthetic, respectively, and increased the values of to 0.887 and 0.915, respectively. The intelligent anesthesia prediction algorithm designed in this study can effectively predict the dosage of anesthetic drugs needed by patients, assist clinical judgment of anesthetic drug dose, and assist the anesthesiologists to ensure the smooth progress of the operation.