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Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

For the problem of low accuracy and applicability of the model prediction in the study of mine water inflow, a method of mine water inflow prediction based on bidirectional short and long memory network(BiLSTM) and deep residual network (DRN) is proposed. First, the data of mine water inflow is processed by wavelet decomposition and normalization to obtain trend item data and detail item data. Secondly, the trend item data was predicted by DRN network method, and the detail item data was predicted by BiLSTM network method. Finally, the two parts of the prediction results will be combined to get the mine water inflow prediction results. The results show that the DRN-BiLSTM model has higher prediction accuracy than a single model, indicating that the model has better generalization.

Macrophages can exhibit pro-inflammatory or pro-reparatory functions, contingent upon their specific activation state. This dynamic behavior empowers macrophages to engage in immune reactions and contribute to tissue homeostasis. Understanding the intricate interplay between macrophage motility and activation status provides valuable insights into the complex mechanisms that govern their diverse functions. In a recent study, we developed a classification method based on morphology, which demonstrated that movement characteristics, including speed and displacement, can serve as distinguishing factors for macrophage subtypes. In this study, we develop a deep learning model to explore the potential of classifying macrophage subtypes based solely on raw trajectory patterns. The classification model relies on the time series of x-y coordinates, as well as the distance traveled and net displacement. We begin by investigating the migratory patterns of macrophages to gain a deeper understanding of their behavior. Although this analysis does not directly inform the deep learning model, it serves to highlight the intricate and distinct dynamics exhibited by different macrophage subtypes, which cannot be easily captured by a finite set of motility metrics. Our study uses cell trajectories to classify three macrophage subtypes: M0, M1, and M2. This advancement holds promising implications for the future, as it suggests the possibility of identifying macrophage subtypes without relying on shape analysis. Consequently, it could potentially eliminate the necessity for high-quality imaging techniques and provide more robust methods for analyzing inherently blurry images.

In this study, a novel fluidized-bed homogeneous granulation (FBHo-G) process was developed to recover lithium (Li) from industrial Li-impacted wastewater. Five important operational variables (i.e., temperatures, pH, [P]0/[Li]0 molar ratios, surface loadings, and up-flow velocities (Umf)) were selected to optimize the Li recovery (TR%) and granulation ratio (GR%) efficiencies of the process. The optimal operational conditions were determined as the following: a temperature of 75 °C, pH of 11.5, [P]0/[Li]0 of 0.5, surface loading of 2.5 kg/m2·h, and Umf of 35.7 m/h). The TR% and GR% at optimal condition could be as much as 90%. The material characterization of the recovery pellet products showed that they were highly crystallized Li3PO4 (purity ~88.2%). The pellets had a round shape and smooth surface with an average size of 0.65 mm, so could easily be stored and transported. The high purity enables them to be further directly reused as raw materials for a wide range of industrial applications (e.g., in the synthesis of cathode materials). Our calculation shows that the FBHo-G process could recover up to 0.1845 kg of lithium per cubic meter of Li-containing wastewater, at a recovery rate of ~90%. A brief technoeconomic analysis shows that FBHG process had economic viability, with an estimate production cost of USD 26/kg Li removed, while the potential gained profit for selling lithium phosphate pellets could be up to USD 48 per the same volume of wastewater and the net profit up to USD 22/m3 Li treated. In all, fluidized-bed homogeneous granulation, a seedless one-step recovery process, opens a promising pathway toward a green and sustainable recycling industry for the recovery and application of the resource-limited lithium element from nonconventional water sources.

Electrical signaling plays a crucial role in the cellular response to tissue injury in wound healing and an external electric field (EF) may expedite the healing process. Here, we have developed a standalone, wearable, and programmable electronic device to administer a well-controlled exogenous EF, aiming to accelerate wound healing in an in vivo mouse model to provide pre-clinical evidence. We monitored the healing process by assessing the re-epithelization rate and the ratio of M1/M2 macrophage phenotypes through histology staining. Following three days of treatment, the M1/M2 macrophage ratio decreased by 30.6% and the re-epithelization in the EF-treated wounds trended towards a non-statically significant 24.2% increase compared to the control. These findings provide point towards the effectiveness of the device in shortening the inflammatory phase by promoting reparative macrophages over inflammatory macrophages, and in speeding up re-epithelialization. Our wearable device supports the rationale for the application of programmed EFs for wound management in vivo and provides an exciting basis for further development of our technology based on the modulation of macrophages and inflammation to better wound healing.

Three kinds of 6061 aluminum alloy cast-rolled plates with Mg/Si mass ratios of 1.23, 1.45, and 1.7 are prepared using NF6–300 vertical twin-roll casting and rolling mill. Metallurgical microscope (OM), x-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive spectrometer (EDS), room temperature tensile test, and other analysis methods studied the influence law and mechanism of Mg/Si ratio on the microstructure and texture of 6061 cast-rolled sheet. The best Mg/Si ratio cast-rolled sheet is then analyzed for formability. Research indicates that with increasing Mg/Si ratio, the grain size shows first decreasing and then increasing trend. Morphology of precipitated phases at the edge and the central of the sheet are different, however, the texture type has no obvious change, and a strong {112} 〈110〉 texture and weaker rotating cube {001} 〈110〉 texture is always observed. When the Mg/Si ratio is 1.45, the average grain size is the smallest, the edge size is 39.78 μ m, the central size is 31.31 μ m, and the precipitated phase is evenly distributed at the edge and central of the cast-rolled sheet. In addition, the elongation reaches 8.9%, the n-value is 0.072, and the r-value is 1.161 when the cast-rolled sheet reaches 90° along the cast-rolled direction under T6 state heat treatment.

Background: Delayed spinal epidural hematoma (SEH) following central neuraxial block (CNB) is a rare but serious complication. The underlying causes of SEH associated with neuraxial anesthesia are still unclear. Furthermore, the decision between surgical intervention and conservative management for SEH remains a complex and unresolved issue. Case Presentation: We report a case of delayed SEH in a 73-year-old woman who underwent vaginal hysterectomy under combined spinal-epidural anesthesia, with the administration of postoperative anticoagulants to prevent deep vein thrombosis on the 1st postoperative day (POD). She experienced symptoms 56 h after CNB. Magnetic resonance imaging (MRI) revealed a dorsal SEH at the L1-L4 level with compression of the thecal sac. On conservative treatment, full recovery was achieved after six months. Conclusions: This case reminds anesthesiologists should be alert to the possible occurrence of a delayed SEH following CNB, particularly with the administration of anticoagulants. Immediate neurological evaluation of neurological deficit and MRI are advised. Conservative treatment combined with close and dynamic neurological function monitoring may be feasible for patients with mild or nonprogressive symptoms even spontaneous recovery. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) is the usual modality for diagnosing stroke, but conventional CT angiography reconstructions have limitations. Methods: A phantom with tubes of known diameters and wall thickness was scanned for wall detectability, wall thickness, and contrast-to-noise ratio (CNR) on conventional and spectral black-blood (SBB) images. The clinical study included 34 stroke patients. Diagnostic certainty and conspicuity of normal/abnormal intracranial vessels using SBB were compared to conventional. Sensitivity/specificity/accuracy of SBB and conventional were compared for plaque detectability. CNR of the wall/lumen and quantitative comparison of remodeling index, plaque burden, and eccentricity were obtained for SBB imaging and high-resolution magnetic resonance imaging (hrMRI). Results: The phantom study showed improved detectability of tube walls using SBB (108/108, 100% versus conventional 81/108, 75%, p < 0.001). CNRs were 75.9 ± 62.6 (mean ± standard deviation) for wall/lumen and 22.0 ± 17.1 for wall/water using SBB and 26.4 ± 15.3 and 101.6 ± 62.5 using conventional. Clinical study demonstrated (i) improved certainty and conspicuity of the vessels using SBB versus conventional (certainty, median score 3 versus 0; conspicuity, median score 3 versus 1 (p < 0.001)), (ii) improved sensitivity/specificity/accuracy of plaque (≥ 1.0 mm) detectability (0.944/0.981/0.962 versus 0.239/0.743/0.495) (p < 0.001), (iii) higher wall/lumen CNR of SBB of (78.3 ± 50.4/79.3 ± 96.7) versus hrMRI (18.9 ± 8.4/24.1 ± 14.1) (p < 0.001), and (iv) excellent reproducibility of remodeling index, plaque burden, and eccentricity using SBB versus hrMRI (intraclass correlation coefficient 0.85–0.94). Conclusions: SBB can enhance the detectability of intracranial plaques with an accuracy similar to that of hrMRI. Relevance statement: This new spectral black-blood technique for the detection and characterization of intracranial vessel atherosclerotic disease could be a time-saving and cost-effective diagnostic step for clinical stroke patients. It may also facilitate prevention strategies for atherosclerosis. Key points: • Blooming artifacts can blur vessel wall morphology on conventional CT angiography. • Spectral black-blood (SBB) images are generated from material decomposition from spectral CT. • SBB images reduce blooming artifacts and noise and accurately detect small plaques. [ABSTRACT FROM AUTHOR]

The vegetative insecticidal protein Vip3Aa from Bacillus thuringiensis (Bt) has been produced by transgenic crops to counter pest resistance to the widely used crystalline (Cry) insecticidal proteins from Bt. To proactively manage pest resistance, there is an urgent need to better understand the genetic basis of resistance to Vip3Aa, which has been largely unknown. We discovered that retrotransposon-mediated alternative splicing of a midgut-specific chitin synthase gene was associated with 5,560-fold resistance to Vip3Aa in a laboratory-selected strain of the fall armyworm, a globally important crop pest. The same mutation in this gene was also detected in a field population. Knockout of this gene via CRISPR/Cas9 caused high levels of resistance to Vip3Aa in fall armyworm and 2 other lepidopteran pests. The insights provided by these results could help to advance monitoring and management of pest resistance to Vip3Aa. The insecticidal protein Vip3Aa is used in genetically engineered crops to overcome pest resistance to other insecticidal transgenes. This study shows that insertion of a transposable element into a gut-specific chitin synthase gene causes Vip3Aa resistance in field-collected individuals of the lepidopteran pest Spodoptera frugiperda (fall armyworm). [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non‐local (NL) block, which has the ability to efficiently capture long‐range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi‐scale context integration and long‐range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U‐Net, an effective crack segmentation model called CrackResU‐Net was developed. The test results on the existing CrackForest dataset showed that CrackResU‐Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state‐of‐the‐art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high‐precision recognition of pavement cracks for engineering purposes. [ABSTRACT FROM AUTHOR]

Background: Fluoride-resistant Streptococcus mutans (S. mutans) strains have developed due to the wide use of fluoride in dental caries prevention. However, the metabolomics of fluoride-resistant S. mutans remains unclear. Objective: This study aimed to identify metabolites that discriminate fluoride-resistant from wild-type S. mutans. Materials and methods: Cell supernatants from fluoride-resistant and wild-type S. mutans were collected and analyzed by liquid chromatography-mass spectrometry. Principal components analysis and partial least-squares discriminant analysis were performed for the statistical analysis by variable influence on projection (VIP > 2.0) and p value (Mann–Whitney test, p < 0.05). Metabolites were assessed qualitatively using the Human Metabolome Database version 2.0 (http://www.hmdb.ca), or Kyoto Encyclopedia of Genes and Genomes (http://www.kegg.jp), and Metaboanalyst 6.0 (https://www.metaboanalyst.ca). Results: Fourteen metabolites differed significantly between fluoride-resistant and wild-type strains in the early log phase. Among these metabolites, 5 were identified. There were 32 differential metabolites between the two strains in the stationary phase, 13 of which were identified. The pyrimidine metabolism for S. mutans FR was matched with the metabolic pathway. Conclusions: The fructose-1,6-bisphosphate concentration increased in fluoride-resistant strains under acidic conditions, suggesting enhanced acidogenicity and acid tolerance. This metabolite may be a promising target for elucidating the cariogenic and fluoride resistant mechanisms of S. mutans. [ABSTRACT FROM AUTHOR]