Application of Surface Wettability to Control Spreading of an Impacting Droplet.

While the simplest outcome of a normal impact on a flat stationary solid surface is radially symmetric spreading, it is important to note that asymmetric spreading can intrinsically occur with a tangential velocity along the surface. However, no previous attempt has been made to restore the symmetry of a lamella that intrinsically spreads asymmetrically. Adjusting the lamella's asymmetric shape to a symmetric one is achieved in this work by varying wettability to affect the receding velocity of the contact line, according to the Taylor-Culick theory. Here we theoretically and practically show how restoring the symmetry can be achieved. Theoretically we built a framework to map the needed receding velocity at every given point of the contact line to allow for symmetry to be restored, and then this framework was applied to generate a wetting map that shows how at each local the wettability of the surface needs to be defined. Simulated results confirmed the effectiveness of our framework and identified the envelope of its applicability. Next, to apply the idea experimentally, the wetting map was transformed to a single wettability contrast area dubbed the "patch". Experimental results showed the effectiveness of the patch design in correcting the asymmetric spreading lamella for water droplets impacting a surface for the following Weber number conditions: Wen ≤ 300, Wet ≤ 300, and 0.51 ≤ Wen/Wet ≤ 2.04.

SALL4 promotes cancer stem-like cell phenotype and radioresistance in oral squamous cell carcinomas via methyltransferase-like 3-mediated m6A modification.

Radioresistance imposes a great challenge in reducing tumor recurrence and improving the clinical prognosis of individuals having oral squamous cell carcinoma (OSCC). OSCC harbors a subpopulation of CD44(+) cells that exhibit cancer stem-like cell (CSC) characteristics are involved in malignant tumor phenotype and radioresistance. Nevertheless, the underlying molecular mechanisms in CD44( + )-OSCC remain unclear. The current investigation demonstrated that methyltransferase-like 3 (METTL3) is highly expressed in CD44(+) cells and promotes CSCs phenotype. Using RNA-sequencing analysis, we further showed that Spalt-like transcription factor 4 (SALL4) is involved in the maintenance of CSCs properties. Furthermore, the overexpression of SALL4 in CD44( + )-OSCC cells caused radioresistance in vitro and in vivo. In contrast, silencing SALL4 sensitized OSCC cells to radiation therapy (RT). Mechanistically, we illustrated that SALL4 is a direct downstream transcriptional regulation target of METTL3, the transcription activation of SALL4 promotes the nuclear transport of ß-catenin and the expression of downstream target genes after radiation therapy, there by activates the Wnt/ß-catenin pathway, effectively enhancing the CSCs phenotype and causing radioresistance. Herein, this study indicates that the METTL3/SALL4 axis promotes the CSCs phenotype and resistance to radiation in OSCC via the Wnt/ß-catenin signaling pathway, and provides a potential therapeutic target to eliminate radioresistant OSCC.

Extraction of eutrophic and green ponds from segmentation of high-resolution imagery based on the EAF-Unet algorithm.

Inland ponds exhibit remarkable ubiquity across the globe, playing a vital role in the sustainability of global continental freshwater resources and contributing significantly to their biodiversity. Numerous ponds are eutrophic and experience recurrent seasonal or year-round algal blooms or persistent duckweed cover, conferring a characteristic green hue. Here, we denote these eutrophic and green ponds as EGPs. The excessive proliferation of algal blooms and duckweed within these EGPs poses a significant threat to the ecological functioning of these aquatic systems, which can lead to hypoxia or the release of microcystins. To identify these EGPs automatically, we constructed an Efficient Attention Fusion Unet (EAF-Unet) algorithm using Gaofen-2 (GF2) panchromatic and multispectral imagery. The attention mechanism was incorporated in Unet to help better detect EGPs. Using the first EGP labeled dataset, we determined the best input feature combination (RGB, NIR, NDVI, and Bright) and the most effective encoding (Rasnet50) for EAF-Unet for distinguishing EGPs from other ground cover types. The evaluation indices - Precision (0.81), Recall (0.79), F1-Score (0.80), and Intersection over Union (IoU, 0.67) - indicate that EAF-Unet can accurately and robustly extract EGPs from GF2 images without relying on pond water masks. Remote-sensing EGP products can assist in identifying ponds with severe eutrophication. Moreover, these products can serve as references for identifying high-risk areas prone to improper sewage discharge or inadequate sewer construction.

Manganese regulation of COPII condensation controls circulating lipid homeostasis.

Precise control of circulating lipids is instrumental in health and disease. Bulk lipids, carried by specialized lipoproteins, are secreted into the circulation, initially via the coat protein complex II (COPII). How the universal COPII machinery accommodates the abundant yet unconventional lipoproteins remains unclear, let alone its therapeutic translation. Here we report that COPII uses manganese-tuning, self-constrained condensation to selectively drive lipoprotein delivery and set lipid homeostasis in vivo. Serendipitously, adenovirus hijacks the condensation-based transport mechanism, thus enabling the identification of cytosolic manganese as an unexpected control signal. Manganese directly binds the inner COPII coat and enhances its condensation, thereby shifting the assembly-versus-dynamics balance of the transport machinery. Manganese can be mobilized from mitochondria stores to signal COPII, and selectively controls lipoprotein secretion with a distinctive, bell-shaped function. Consequently, dietary titration of manganese enables tailored lipid management that counters pathological dyslipidaemia and atherosclerosis, implicating a condensation-targeting strategy with broad therapeutic potential for cardio-metabolic health.

Innovative Solid Slippery Coating: Uniting Mechanical Durability, Optical Transparency, Anti-Icing, and Anti-Graffiti Traits.

Slippery coatings, such as the slippery liquid-infused porous surface (SLIPS), have gained significant attention for their potential applications in anti-icing and anti-fouling. However, they lack durability when subjected to mechanical impact. In this study, we have developed a robust slippery coating by blending polyurethane acrylate (PUA) with methyltriethoxysilane (MTES) and perfluoropolyether (PFPE) in the solvent of butyl acetate. The resulting mixture is homogeneous and allows for uniform coating on various substrates using a drop coating process followed by drying at 160 °C for 3 h. The cured coating exhibits excellent water repellency (contact angle of ~108° and sliding angle of ~8°), high transparency (average visible transmittance of ~90%), exceptional adherence to the substrate (5B rating according to ASTMD 3359), and remarkable hardness (4H on the pencil hardness scale). Moreover, the coating is quite flexible and can be folded without affecting its wettability. The robustness of the coating is evident in its ability to maintain a sliding angle below 25° even when subjected to abrasion, water jetting, high temperature, and UV irradiation. Due to its excellent nonwetting properties, the coating can be employed in anti-icing, anti-graffiti, and anti-sticking applications. It effectively reduces ice adhesion on aluminum substrates from approximately 217 kPa to 12 kPa. Even after 20 cycles of icing and de-icing, there is only a slight increase in ice adhesion, stabilizing at 40 kPa. The coating can resist graffiti for up to 400 cycles of writing with an oily marker pen and erasing with a tissue. Additionally, the coating allows for easy removal of 3M tape thereon without leaving any residue.

A biocompatible pure organic porous nanocage for enhanced photodynamic therapy.

Porphyrin-based photosensitizers have been widely utilized in photodynamic therapy (PDT), but they suffer from deteriorating fluorescence and reactive oxygen species (ROS) due to their close π-π stacking. Herein, a biocompatible pure organic porphyrin nanocage (Py-Cage) with enhanced both type I and type II ROS generation is reported for PDT. The porphyrin skeleton within the Py-Cage is spatially separated by four biphenyls to avoid the close π-π stacking within the nanocage. The Py-Cage showed a large cavity and high porosity with a Brunauer-Emmett-Teller surface area of over 300 m2 g-1, facilitating a close contact between the Py-Cage and oxygen, as well as the fast release of ROS to the surrounding microenvironment. The Py-Cage shows superb ROS generation performance over its precursors and commercial ones such as Chlorin E6 and Rose Bengal. Intriguingly, the cationic π-conjugated Py-Cage also shows promising type I ROS (superoxide and hydroxyl radicals) generation that is more promising for hypoxic tumor treatment. Both in vitro cell and in vivo animal experiments further confirm the excellent antitumor activity of the Py-Cage. As compared to conventional metal coordination approaches to improve PDT efficacy of porphyrin derivatives, the pure organic porous Py-Cage demonstrates excellent biocompatibility, which is further verified in both mice and rats. This work of an organic porous nanocage shall provide a new paradigm for the design of novel, biocompatible and effective photosensitizers for PDT.

[Advances on the microbial synthesis of plant-derived diterpenoids].

Natural plant-derived diterpenoids are a class of compounds with diverse structures and functions. These compounds are widely used in pharmaceuticals, cosmetics and food additives industries because of their pharmacological properties such as anticancer, anti-inflammatory and antibacterial activities. In recent years, with the gradual discovery of functional genes in the biosynthetic pathway of plant-derived diterpenoids and the development of synthetic biotechnology, great efforts have been made to construct a variety of diterpenoid microbial cell factories through metabolic engineering and synthetic biology, resulting in gram-level production of many compounds. This article summarizes the construction of plant-derived diterpenoid microbial cell factories through synthetic biotechnology, followed by introducing the metabolic engineering strategies applied to improve plant-derived diterpenoids production, with the aim to provide a reference for the construction of high-yield plant-derived diterpenoid microbial cell factories and the industrial production of diterpenoids.

Multiscale Deepspectra Network: Detection of Pyrethroid Pesticide Residues on the Hami Melon.

The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380-1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of "5 × 1" and "3 × 1" kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis.

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging.

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000-2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.

Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model.

BACKGROUND AND OBJECTIVES: Atrial fibrillation (AF) is one of the most common arrhythmias clinically. Aging tends to increase the risk of AF, which also increases the burden of other comorbidities, including coronary artery disease (CAD), and even heart failure (HF). The precise detection of AF is a challenge due to its intermittence and unpredictability. A method for the accurate detection of AF is still needed. METHODS: A deep learning model was used to detect atrial fibrillation. Here, a distinction was not made between AF and atrial flutter (AFL), both of which manifest as a similar pattern on an electrocardiogram (ECG). This method not only discriminated AF from normal rhythm of the heart, but also detected its onset and offset. The proposed model involved residual blocks and a Transformer encoder. RESULTS AND CONCLUSIONS: The data used for training were obtained from the CPSC2021 Challenge, and were collected using dynamic ECG devices. Tests on four public datasets validated the availability of the proposed method. The best performance for AF rhythm testing attained an accuracy of 98.67%, a sensitivity of 87.69%, and a specificity of 98.56%. In onset and offset detection, it obtained a sensitivity of 95.90% and 87.70%, respectively. The algorithm with a low FPR of 0.46% was able to reduce troubling false alarms. The model had a great capability to discriminate AF from normal rhythm and to detect its onset and offset. Noise stress tests were conducted after mixing three types of noise. We visualized the model's features using a heatmap and illustrated its interpretability. The model focused directly on the crucial ECG waveform where showed obvious characteristics of AF.

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion.

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.

Study on pollution evolution law of coal and rock mixed dust in coal-oil shale fully mechanized mining face: a study case.

To better understand the dust dispersion and pollution laws in coal-oil shale fully mechanized mining faces, the airflow distribution and coal and oil shale mixed dust emission law was simulated, and the simulation results are analyzed and verified in combination with the field measured data. The research results showed that in the area 0-10 m on the leeward side of the front drum, most of the coal dust particles with a large particle size stay near the roof of the hydraulic support and the height of the breathing zone, while most of the oil shale dust particles with a large particle size stay in the area below the height of the breathing zone. In the height of the breathing belt, oil shale and coal dust particles seriously polluted the 0-6-m and 0-13-m areas on the leeward side of the front drum of the shearer, respectively. According to the different distribution of coal dust and oil shale dust, a wet dust collector and multi-nozzle atomization set are designed to remove dust. The field test results show that the dust removal rates of the two kinds of dust reach 83.4% and 87.5% respectively after the dust removal device is opened.

Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+.

Computerized tomography (CT) is of great significance for the localization and diagnosis of liver cancer. Many scholars have recently applied deep learning methods to segment CT images of liver and liver tumors. Unlike natural images, medical image segmentation is usually more challenging due to its nature. Aiming at the problem of blurry boundaries and complex gradients of liver tumor images, a deep supervision network based on the combination of high-efficiency channel attention and Res-UNet++ (ECA residual UNet++) is proposed for liver CT image segmentation, enabling fully automated end-to-end segmentation of the network. In this paper, the UNet++ structure is selected as the baseline. The residual block feature encoder based on context awareness enhances the feature extraction ability and solves the problem of deep network degradation. The introduction of an efficient attention module combines the depth of the feature map with spatial information to alleviate the uneven sample distribution impact; Use DiceLoss to replace the cross-entropy loss function to optimize network parameters. The liver and liver tumor segmentation accuracy on the LITS dataset was 95.8% and 89.3%, respectively. The results show that compared with other algorithms, the method proposed in this paper achieves a good segmentation performance, which has specific reference significance for computer-assisted diagnosis and treatment to attain fine segmentation of liver and liver tumors.

Pollution and occupational protection of diesel particulate matter in underground space.

To address the diesel particulate matter pollution problem at the 12,306 continuous mining face of Shangwan coal mine, the spatial and temporal evolution law of diesel particulate matter generated at the three locations of the shuttle car head tunnel, contact alley, and support tunnel under the pressure-in ventilation condition of the double lane of the continuous mining face was studied by numerical simulation. The results show that the highest diesel particulate matter concentration at the shuttle car discharge is about 144.17 mg/m3, which seriously affects the health of miners. The highest diesel particulate matter concentration at the shuttle car tunnel is 52.58 mg/m3, and at the contact alley, the diesel particulate matter diffusion space is limited by the compression of the space inside the contact alley by the shuttle car machine body and the alley wall, which makes the diesel particulate matter accumulate here, forming a high diesel particulate matter concentration distribution area with a concentration value of 112.75 mg/m3. When supporting the roadway at the shuttle, diesel particulate matter accumulates in the range of X = 55 m ~ 60 m, Y = 0 m ~ 4 m, and Z = 23.4 m ~ 29.4 m. According to the degree of DPM pollution in different areas, different individual protective equipment is used to obtain different levels of pollution protection.

Numerical simulation investigation on optimal air volume for dilution dust-gas by forced ventilation in fully mechanized driving face.

In order to ensure the best dust removal on the basis of the optimal gas emission, and to determine the best dust and gas exhaust air volume, numerical simulation research was carried out on the airflow-dust-gas field of the fully mechanized driving face. The results indicate that under different air volumes, with an increase in the distance from the head-on, the airflow velocity of the fully mechanized driving face first increased and then decreased, and gradually tended to be stable. When Q = 800-900 m3/min, the head-on gas dilution ability is strong and the range of high gas content was the minimum. When Q > 900 m3/min, the gas dilution efficiency was reduced and easy to cause secondary dust. In the height of the respiratory zone, the relationship between the dust concentration distribution and air volume is [Formula: see text], and that between the gas content and air volume is [Formula: see text]. Finally, the optimal air volume range was determined to be Q = 800-900 m3/min. By comparing the measured and simulated airflow velocity, dust concentration, and gas content, the average errors were 6.77%, 6.83%, and 7.73%, respectively, which proves the reliability of the numerical simulation results.

Development of a Multi-Channel Wearable Heart Sound Visualization System.

A multi-channel wearable heart sound visualization system based on novel heart sound sensors for imaging cardiac acoustic maps was developed and designed. The cardiac acoustic map could be used to detect cardiac vibration and heart sound propagation. The visualization system acquired 72 heart sound signals and one ECG signal simultaneously using 72 heart sound sensors placed on the chest surface and one ECG analog front end. The novel heart sound sensors had the advantages of high signal quality, small size, and high sensitivity. Butterworth filtering and wavelet transform were used to reduce noise in the signals. The cardiac acoustic map was obtained based on the cubic spline interpolation of the heart sound signals. The results showed the heart sound signals on the chest surface could be detected and visualized by this system. The variations of heart sounds were clearly displayed. This study provided a way to select optimal position for auscultation of heart sounds. The visualization system could provide a technology for investigating the propagation of heart sound in the thoracic cavity.

Discrimination of Pesticide Residue Levels on the Hami Melon Surface Using Multiscale Convolution.

Pesticide residues directly or indirectly threaten the health of humans and animals. We need a rapid and nondestructive method for the safety evaluation of fruits. In this study, the feasibility of visible/near-infrared (Vis/NIR) spectroscopy technology was explored for the discrimination of pesticide residue levels on the Hami melon surface. The one-dimensional convolutional neural network (1D-CNN) model was proposed for spectral data discrimination. We compared the effect of different convolutional architectures on the model performance, including single-depth, symmetric, and asymmetric multiscale convolution. The results showed that the 1D-CNN model could discriminate the presence or absence of pesticide residues with a high accuracy above 99.00%. The multiscale convolution could significantly improve the model accuracy while reducing the modeling time. In particular, the asymmetric convolution had a better comprehensive performance. For two-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 93.68% and 95.79%, respectively. For three-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 86.32% and 89.47%, respectively. For four-level discrimination, the accuracy of lambda-cyhalothrin and beta-cypermethrin was 87.37% and 93.68%, respectively, and the average modeling time was 3.5 s. This finding will encourage more relevant research to use multiscale 1D-CNN as a spectral analysis strategy for the detection of pesticide residues in fruits.