Investigation on the band narrowing and shifting effects of micro-perforated panel absorbers.

Micro-perforated panel (MPP) absorbers exhibit multiple resonance bands with increased bandwidth narrowing and shifting in higher frequencies, limiting their effectiveness. This study investigates the effects of narrowing and shifting in higher-order resonance bands of MPP absorbers. First, an acoustic impedance model for MPP absorbers is introduced, and the narrowing and shifting coefficients are defined and modeled to quantify these effects. It is observed that a larger ratio of acoustic resistance to acoustic mass is favorable for reducing the narrowing and shifting effects. Subsequently, the theoretical model is validated using a numerical model, and a parametric study is conducted to explore the influence of geometric parameters on the narrowing and shifting effects. The study reveals that decreasing aperture and panel thickness, while increasing perforation ratio and cavity depth, reduces the narrowing and shifting coefficients. Remarkably, ultra-micro-perforated panels (UMPPs) with an aperture below 0.1 mm and perforation constant below 0.0046, having relatively larger acoustic resistance and smaller acoustic mass, demonstrate near-zero band narrowing and shifting. Finally, UMPPs are fabricated using micro-electro-mechanical systems (MEMS) technology, and their normal absorption coefficients are measured. Results align with theoretical predictions, confirming UMPPs' ability to achieve zero narrowing and shifting compared to ordinary MPPs and verifying the study's findings.

Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT.

PURPOSE: Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves' orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. MATERIALS AND METHODS: GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM). RESULTS: The GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 (p < 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. CONCLUSION: The proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO.

A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images.

Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for assessing atherosclerosis and the degree of stenosis. Accurate segmentation and lumen and median-adventitia (MA) measurements from IVUS are essential for such a successful clinical evaluation. However, current automated segmentation by commercial software relies on manual corrections, which is time-consuming and user-dependent. We aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically and accurately extract both lumen and MA border. Inspired by the dual-path design of the state-of-the-art model IVUS-Net, our method named IVUS-U-Net++ achieved an extension of the U-Net++ model. More specifically, a feature pyramid network was added to the U-Net++ model, enabling the utilization of feature maps at different scales. Following the segmentation, the Pearson correlation and Bland-Altman analyses were performed to evaluate the correlations of 12 clinical parameters measured from our segmentation results and the ground truth. A dataset with 1746 IVUS images from 18 patients was used for training and testing. Our segmentation model at the patient level achieved a Jaccard measure (JM) of 0.9080 ± 0.0321 and a Hausdorff distance (HD) of 0.1484 ± 0.1584 mm for the lumen border; it achieved a JM of 0.9199 ± 0.0370 and an HD of 0.1781 ± 0.1906 mm for the MA border. The 12 clinical parameters measured from our segmentation results agreed well with those from the ground truth (all p-values are smaller than .01). Our proposed method shows great promise for its clinical use in IVUS segmentation.

Biochemical insights into the biodegradation mechanism of typical sulfonylureas herbicides and association with active enzymes and physiological response of fungal microbes: A multi-omics approach.

The extensive use of sulfonylurea herbicides has raised major concerns regarding their long-term soil residues and agroecological risks despite their role in agricultural protection. Microbial degradation is an important approach to remove sulfonylureas, whereas understanding the associated biodegradation mechanisms, enzymes, and physiological responses remains incomplete. Based on the rapid biodegradation of nicosulfuron by typical fungal isolate Talaromyces flavus LZM1, the dependency on cellular accumulation and environmental conditions, e.g. pH and nutrient supplies, was shown in the study. The biodegradation of nicosulfuron occurred intracellularly and followed the cascade of reactions including hydrolysis, Smile contraction rearrangement, hydroxylation, and opening of the pyrimidine ring. Besides 2-amino-4,6-dimethoxypyrimidine (ADMP) and 2-aminosulfonyl-N,N-dimethylnicotinamide (ASDM), numerous products and intermediates were newly identified and the structural forms of methoxypyrimidine and sulfonylurea bridge contraction rearrangement are predicted to be more toxic than nicosulfuron. The biodegradation should be enzymatically regulated by glycosylphosphatidylinositol transaminase (GPI-T) and P450s, which were manifested with the significant upregulation in proteomics. It is the first time that the hydrolysis of nicosulfuron into ADMP and ASDM have been associated with GPI-T. The integrated pathways of biodegradation were further elucidated through the involvement of various active enzymes. Except for the enzymatic catalysis, the physiological responses verified by metabolo-proteomics were critical not only to regulate material synthesis, uptake, utilization, and energy transfer but also to maintain antioxidant homeostasis, biodegradability, and tolerance of nicosulfuron by the differentially expressed metabolites, such as acetolactate synthase and 3-isopropylmalate dehydratase. The obtained results would help understand the biodegradation mechanism of sulfonylurea from chemicobiology and enzymology and promote the use of fungal biodegradation in pollution rehabilitation.

Bi Nanosheets on Porous Carbon Cloth Composites for Ultrastable Flexible Nickel-Bismuth Batteries.

The use of bismuth (Bi) as an anode material in nickel-metal batteries has gained significant attention due to its highly reversible redox reaction and suitable operating conditions. However, the cycling stability and flexibility of nickel-bismuth (Ni//Bi) batteries need to be further improved. This paper employs a facile electrodeposition technique to prepare Bi nanosheets uniformly grown on a porous carbon cloth (PCC), denoted as Bi-PCC electrodes. The Bi-PCC electrode portrays a specific surface area and good wettability that enable fast charge transfer and ion transport channels. Consequently, the Bi-PCC electrode demonstrates a high specific capacity of up to 297.1 mAh g-1 at 2 A g-1, with a capacity retention of up to 71.5% at 2-40 A g-1 and an impressive capacity retention of 79.9% after 1000 cycles at 2-40 A g-1. More importantly, the flexible rechargeable Ni//Bi battery (denoted as Ni(OH)2-PCC//Bi-PCC) with Bi-PCC as the anode and Ni(OH)2-PCC as the cathode has excellent electrochemical performance. The Ni(OH)2-PCC//Bi-PCC battery boasts a remarkable capacity retention of 93.6% after 3000 cycles at 10 A g-1. Further, the cell presents a maximum energy density of 73.1 Wh kg-1 and an impressive power density of 11.9 kW kg-1.

A state-of-the-art liposome technology for glioblastoma treatment.

Glioblastoma (GBM) is a challenging problem due to the poor BBB permeability of cancer drugs, its recurrence after the treatment, and high malignancy and is difficult to treat with the currently available therapeutic strategies. Furthermore, the prognosis and survival rate of GBM are still poor after surgical removal via conventional combination therapy. Owing to the existence of the formidable blood-brain barrier (BBB) and the aggressive, infiltrating nature of GBM growth, the diagnosis and treatment of GBM are quite challenging. Recently, liposomes and their derivatives have emerged as super cargos for the delivery of both hydrophobic and hydrophilic drugs for the treatment of glioblastoma because of their advantages, such as biocompatibility, long circulation, and ease of physical and chemical modification, which facilitate the capability of targeting specific sites, circumvention of BBB transport restrictions, and amplification of the therapeutic efficacy. Herein, we provide a timely update on the burgeoning liposome-based drug delivery systems and potential challenges in these fields for the diagnosis and treatment of brain tumors. Furthermore, we focus on the most recent liposome-based drug delivery cargos, including pH-sensitive, temperature-sensitive, and biomimetic liposomes, to enhance the multimodality in imaging and therapeutics of glioblastoma. Furthermore, we highlight the future difficulties and directions for the research and clinical translation of liposome-based drug delivery. Hopefully, this review will trigger the interest of researchers to expedite the development of liposome cargos and even their clinical translation for improving the prognosis of glioblastoma.

Congener-specificity, dioxygenation dependency and association with enzyme binding for biodegradation of polybrominated diphenyl ethers by typical aerobic bacteria: Experimental and theoretical studies.

Polybrominated diphenyl ethers (PBDEs) are a group of organic pollutants that have attracted much concerns of scientific community over the ubiquitous distribution, chemical persistence and toxicological risks in the environment. Though a great number of aerobic bacteria have been isolated for the rapid removal of PBDEs, the knowledge about biodegradation characteristics and mechanism is less provided yet. Herein, the congener-specificity of aerobic biodegradation of PBDEs by typical bacteria, i.e. B. xenovorans LB400 was identified with the different biodegradation kinetics, of which the changes were largely hinged on the bromination pattern. The more bromination isomerically at ortho-sites other than meta-sites or the single bromination at one of aromatic rings might always exert the positive effect. The biodegradation of PBDEs should be thermodynamically constrained to some extent because the calculated Gibbs free energy changes of initial dioxygenation by quantum chemical method increased with the increase of bromination. Within the transition state theory, the high correlativity between the apparent biodegradation rates and Gibbs free energy changes implied the predominance and rate-limiting character of initial dioxygenation, while the regioselectivity of dioxygenation at the ortho/meta-sites was also manifested for the more negative charge population. The molecular binding with the active domain of dioxygenase BphA1 in aerobe was firstly investigated using docking approach. As significantly illustrated with the positive relationship, the higher binding affinity with BphA1 should probably signify the more rapid biodegradation. Besides the edge-on π-π stacking of PBDEs with F227 or Y277 and π-cation formulation with histidines (H233, H239) in BphA1, the reticular hydrophobic contacts appeared as the major force to underpin the high binding affinity and rapid biodegradation of PBDEs. Overall, the experimental and theoretical results would not only help understand the aerobic biodegradation mechanism, but facilitate enhancing applicability or strategy development of engineering bacteria for bioremediation of PBDEs in the environment.

Degradation of mineral-immobilized pyrene by ferrate oxidation: Role of mineral type and intermediate oxidative iron species.

Ferrate (Fe(VI)) salts like K2FeO4 are efficient green oxidants to remediate organic contaminants in water treatment. Minerals are efficient sorbents of contaminants and also excellent solid heterogeneous catalysts which might affect Fe(VI) remediation processes. By targeting the typical polycyclic aromatic hydrocarbon compound - pyrene, the application of Fe(VI) for oxidation of pyrene immobilized on three minerals, i.e., montmorillonite, kaolinite and goethite was studied for the first time. Pyrene immobilized on the three minerals was efficiently oxidized by Fe(VI), with 87%-99% of pyrene (10 µM) being degraded at pH 9.0 in the presence of a 50-fold molar excess Fe(VI). Different minerals favored different pH optima for pyrene degradation, with pH optima from neutral to alkaline following the order of montmorillonite (pH 7.0), kaolinite (pH 8.0), and goethite (pH 9.0). Although goethite revealed the highest catalytic activity on pyrene degradation by Fe(VI), the greater noneffective loss of the oxidative species by ready self-decay in the goethite system resulted in lower degradation efficiency compared to montmorillonite. Protonation and Lewis acid on montmorillonite and goethite assisted Fe(VI) oxidation of pyrene. The intermediate ferrate species (Fe(V)/Fe(IV)) were the dominant oxidative species accountable for pyrene oxidation, while the contribution of Fe(VI) species was negligible. Hydroxyl radical was involved in mineral-immobilized pyrene degradation and contributed to 11.5%-27.4% of the pyrene degradation in montmorillonite system, followed by kaolinite (10.8%-21.4%) and goethite (5.1%-12.2%) according to the hydroxyl radical quenching experiments. Cations abundant in the matrix and dissolved humic acid hampered pyrene degradation. Finally, two different degradation pathways both producing phthalic acid were identified. This study demonstrates efficient Fe(VI) oxidation of pyrene immobilized on minerals and contributes to the development of efficient environmentally friendly Fe(VI) based remediation techniques.

Severity detection of COVID-19 infection with machine learning of clinical records and CT images.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.

Effects of Process Parameters on the Thickness Uniformity in Two-Point Incremental Forming (TPIF) with a Positive Die for an Irregular Stepped Part.

Incremental sheet forming (ISF) is a novel flexible forming technology with advantages, such as a low forming force, low-energy-consuming equipment, and good forming performance. The lack of available information about the formability of the two-point incremental forming (TPIF) process makes it limited for practical applications. Taking an irregular stepped part as the target part, the effects of process parameters on the thickness uniformity when using TPIF with a positive die for AA1060 aluminum alloy sheets were investigated. First, the set of optimal parameters regarding the diameter of the tool head, feed rate, and the step size were obtained through orthogonal experiments. Furthermore, the optimal parameter set of the number of forming passes, the direction of movement of the forming tool, and the forming angle was determined and the optimal forming result was numerically and experimentally verified. This demonstrated that the parameters affecting the thickness uniformity of the irregular stepped parts were, in descending order, the diameter of the forming tool, the feed rate, and the step size, with corresponding optimal values of 12 mm, 15,000 mm/min, and 0.4 mm, respectively. With an increase of the number of passes and a decrease of the forming angle between adjacent passes, and adopting an alternating clockwise and counterclockwise toolpath, the thickness uniformity of the formed parts was effectively improved.

Effect of heat treatment under vacuum on structure and visible-light photocatalytic activity of nano-TiO2.

Nano-TiO2 is known as a photocatalyst with high catalytic activity. However, it should be emphasized that the bandgap of nano-TiO2 is wide, which limits its photocatalytic efficiency in response to visible light and thus hinders its potential application. Improving the photocatalytic activity of nano-TiO2 under visible light by the strategy of heat treatment under vacuum was investigated in this study. The structure and photocatalytic activity of nano-TiO2 before and after heat treatment under vacuum were compared and analyzed by XRD, TEM, HRTEM, XPS and UV-Vis-NIR, respectively. The results show that oxygen vacancies were introduced into the crystal structure of nano-TiO2 to change its inherent energy band structure. Particularly, the samples after heat treatment under vacuum exhibited high photocatalytic activity under visible light. In addition, the formation mechanism of non-stoichiometric compound TiO2-x and the mechanism of oxygen vacancy defects to expand the wavelength of light that nano-TiO2 absorbs to the visible portion of the spectrum have also been addressed in this paper.

Achieving High Strength and Good Ductility in As-Extruded Mg-Gd-Y-Zn Alloys by Ce Micro-Alloying.

In this study, the effect of Ce additions on microstructure evolution of Mg-7Gd-3.5Y-0.3Zn (wt %) alloys during the casting, homogenization, aging and extrusion processing are investigated, and novel mechanical properties are also obtained. The results show that Ce addition promotes the formation of long period stacking ordered (LPSO) phases in the as-cast Mg-Gd-Y-Zn-Ce alloys. A high content of Ce addition would reduce the maximum solubility of Gd and Y in the Mg matrix, which leads to the higher density of Mg12Ce phases in the as-homogenized alloys. The major second phases observed in the as-extruded alloys are micron-sized bulk LPSO phases, nano-sized stripe LPSO phases, and broken Mg12Ce and Mg5RE phases. Recrystallized grain size of the as-extruded 0.2Ce, 0.5Ce and 1.0Ce alloys can be refined to ~4.3 µm, ~1.0 µm and ~8.4 µm, respectively, which is caused by the synthesized effect of both micron phases and nano phases. The strength and ductility of as-extruded samples firstly increase and then decrease with increasing Ce content. As-extruded 0.5Ce alloy exhibits optimal mechanical properties, with ultimate strength of 365 MPa and ductility of ~15% simultaneously.