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The quantitative characterization of coal structure is one of the hot topics in coal reservoir research. To finely characterize the spatial distribution of coal structure and identify the main controlling factors for its differential distribution, through observation and measurement of coal cores, quantitative characterization of coal structure using geological intensity factors, and statistics of logging response of different coal structures, a quantitative characterization model of coal structure based on logging curves was constructed. The spatial distribution of coal structure was predicted, and the influence characteristics of sedimentation, structure, in-situ stress, and micro-mechanical properties on coal structure were explored. The research results indicate that density, gamma-ray, interval transit time, and caliper loggings are sensitive to the coal structure, and the use of logging multiple regression method can significantly improve the accuracy of coal structure identification. The coals in the study area are mainly composed of primary-fragmented coals, followed by cataclastic coals, with a small amount of primary and cataclastic-granulated coals developed. The coal structure of the entire study area can be divided into four categories and seven small areas. The sedimentary environment can affect the coal structure by controlling the thickness of coal seam development. The thickness of coal seams developed in tidal channels, sand flats, mud flats, delta plains, and lagoon environments gradually increases, but there is no obvious relationship between coal structure and coal seam thickness. However, the ash content is positively correlated with the integrity of coal structure, and studying the changes in ash content in different sedimentary environments is the key to revealing the control characteristics of sedimentary environments on coal structure. The average structural curvature of primary structured coals, primary- fragmented structured coals, and cataclastic coals are 8.4×10−6, 18.7×10−6, 25.7×10−6 m−1, respectively. Where faults develop, the coal structure is characterized by cataclastic coals. As the burial depth increases, the in-situ stress increases, and the integrity of the coal structures are enhanced. Based on this, the in-situ stress state further differentiates the coal structures. In the shallow extensional and compression transition zone, the coal structure is mainly fragmented and fragmented; In the deep compression state, the primary fragmented structure is dominant; The proportion of fractured structures in the deep compression and extension transition zone has increased. The micro mechanical properties of coal are controlled by the composition of coal matrix and micro pore structure. The mechanical strength of inorganic components of coal is higher than that of organic components of coal, and the pore size of inorganic components is smaller than that of organic components. The higher the content of inorganic components, the higher the mechanical strength of coal, and the more complete the coal structure. This research achievement provides reference for coal seam drilling construction, fracturing transformation, and reservoir evaluation.

Abstract Background Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients. Methods We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting. Results The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results. Conclusions The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.

Porous β-Ti composites hold large promise for lightweight components due to their exceptional strength-ductility synergy. However, their microstructural behavior under both impact and compressive conditions have received limited attention. This study investigates the compressive and impact behavior of porous β-type Ti–25Nb (at.%) composites fabricated using laser powder bed fusion (LPBF). The composites feature a rhombic dodecahedron (RD) lattice structure and incorporate a central solid cylinder with varying diameters. Microstructural characterizations and finite element simulations were employed to analyze mechanical properties and stress distribution. Incorporation of 3 mm radius cylinders (TIII ∼45% porosity) significantly enhances compressive yield stress (88 ± 10 MPa), impact yield stress (153 ± 10 MPa), and impact energy absorption (41.2 J/cm³) while maintaining material plasticity. Compressive and impact loading initiate a martensitic transformation from β to α″ phase, strengthening the alloy through refined α″ formation with a finer grain size than β phase. Notably, a β→ α″ transformation and grain refinement occur in the node region that connects the solid cylinder and lattices, which undergoes significant deformation during impact. This observation suggests the impact resistance of porous β-Ti composites can be enhanced through tailored microstructures and strategic structural design.

Abstract Background Fleas are one of the most common and pervasive ectoparasites worldwide, comprising at least 2500 valid species. They are vectors of several disease-causing agents, such as Yersinia pestis. Despite their significance, however, the molecular genetics, biology, and phylogenetics of fleas remain poorly understood. Methods We sequenced, assembled, and annotated the complete mitochondrial (mt) genome of the rodent flea Nosopsyllus laeviceps using next-generation sequencing technology. Then we combined the new mitogenome generated here with mt genomic data available for 23 other flea species to perform comparative mitogenomics, nucleotide diversity, and evolutionary rate analysis. Subsequently, the phylogenetic relationship within the order Siphonaptera was explored using the Bayesian inference (BI) and maximum likelihood (ML) methods based on concentrated data for 13 mt protein-coding genes. Results The complete mt genome of the rodent flea N. laeviceps was 16,533 base pairs (bp) in a circular DNA molecule, containing 37 typical genes (13 protein-coding genes, 22 transfer RNA [tRNA] genes, and two ribosomal RNA [rRNA] genes) with one large non-coding region (NCR). Comparative analysis among the order Siphonaptera showed a stable gene order with no gene arrangement, and high AT content (76.71–83.21%) with an apparent negative AT and GC skew except in three fleas Aviostivalius klossi bispiniformis, Leptopsylla segnis, and Neopsylla specialis. Moreover, we found robust evidence that the cytochrome c oxidase subunit 1 (cox1) gene was the most conserved protein-coding gene (Pi = 0.15, non-synonymous/synonymous [Ka/Ks] ratio = 0.13) of fleas. Phylogenomic analysis conducted using two methods revealed different topologies, but both results strongly indicated that (i) the families Ceratophyllidae and Leptopsyllidae were paraphyletic and were the closest to each other, and (ii) the family Ctenophthalmidae was paraphyletic. Conclusions In this study, we obtained a high-quality mt genome of the rodent flea N. laeviceps and performed comparative mitogenomics and phylogeny of the order Siphonaptera using the mt database. The results will enrich the mt genome data for fleas, lay a foundation for the phylogenetic analysis of fleas, and promote the evolutionary analysis of Siphonaptera. Graphical Abstract

With the increasing availability of diverse healthcare data sources, such as medical images and electronic health records, there is a growing need to effectively integrate and fuse this multimodal data for comprehensive analysis and decision-making. However, despite its potential, multimodal data fusion in healthcare remains limited. This review paper provides an overview of existing literature on multimodal data fusion in healthcare, covering 69 relevant works published between 2018 and 2024. It focuses on methodologies that integrate different data types to enhance medical analysis, including techniques for integrating medical images with structured and unstructured data, combining multiple image modalities, and other features. Additionally, the paper reviews various approaches to multimodal data fusion, such as early, intermediate, and late fusion methods, and examines the challenges and limitations associated with these techniques. The potential benefits and applications of multimodal data fusion in various diseases are highlighted, illustrating specific strategies employed in healthcare artificial intelligence (AI) model development. This research synthesizes existing information to facilitate progress in using multimodal data for improved medical diagnosis and treatment planning.

Microtus fortis is known as a non-susceptible animal host of S. japonicum. A better understanding of this animal immune defense mechanism during the early stage of infection may offer an alternative route for vaccine development or therapy. Here, we analyzed the whole blood transcriptome of M. fortis using next-generation sequencing (NGS) to identify immune genes of biological relevance that might be involved in the mechanism of its resistance. The blood samples were collected from uninfected animals (control group) and infected animals at different time points (3, 7, 10 and 14 days post-infection). We identified 5310 sequences as unigenes and successfully annotated 4636 of them. The immune response was more intense at 10 dpi. The upregulated genes at this time point were mainly activated in the TNF and NF-kappa B signaling pathways, Th1, Th2and Th17 cell differentiation as well as cytokine-cytokine receptor interaction. Based on the differentially expressed genes analysis, we report that the IF27L2B, RETN, PGRP, IFI35, TYROBP, S100A8, S100A11, CD162, CD88, CYBA, and LBP could play important roles in the mechanism of M. fortis resistance.

Hydrogels have indeed achieved significant advancements, yet their clinical translation has been hampered by their inherent limitations in wet adhesion properties. Furthermore, the design of adhesive hydrogels that can resist postoperative adhesions remains an intricate challenge. In this study, we introduce a Janus hydrogel (JGP) that offers a novel approach to address these challenges. The JGP hydrogel has two asymmetrical sides, consisting of an adhesion layer (AL) and an anti-adhesion layer (AAL). Specifically, the AL incorporates three key components: N-[tris(hydroxymethyl)methyl]acrylamide (THMA), acrylic acid (AAc), and the acrylic acid N-hydroxysuccinimide ester (AAc-NHS). By drying the AL, it has a rapid water absorption capability. The abundance of hydroxyl and carboxyl groups in the AL enables the formation of robust hydrogen bonds with tissues, thereby achieving superior adhesive properties. Additionally, the synergistic effect of THMA's tridentate hydrogen bonding and the covalent bonding formed by AAc-NHS with tissue ensures long-lasting wet adhesion. To realize the anti-adhesion function, one side of the AL was immersed in a solution of [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SBMA), which undergoes crosslinking to form the AAL. A comprehensive series of tests have confirmed that the JGP hydrogel exhibits exceptional mechanical properties, efficient and enduring adhesion, excellent biocompatibility, and degradability. Moreover, it possesses remarkable hemostatic properties and robust anti-abdominal adhesion characteristics.

Abstract Fabry-Pérot (FP) microcavities have attracted tremendous attention in recent years due to their favorable optical characteristics of the high quality (Q) factor and small mode volume. In this work, we presented a novel approach that utilized the soft lithography and imprinting technology to incorporate the convex micro-lens array structure into the FP (FP-lens) cavity. A strong mode-profile restriction of the micro-lens simultaneously reduced the mode volume and enhanced the Q factor, exhibiting high tolerance to non-parallelism of mirrors compared with that of the plane-plane FP (PP-FP) microcavities. In the experiment, the Q factor of the FP-lens cavity was measured to be 8.145×104, which exhibited a 5.6-fold increase than that of the PP-FP cavity. Furthermore, we experimentally measured the refractive index sensing performance of the FP-lens cavity with the sensitivity of 594.7 nm/RIU and a detection limit of 4.26×10−7 RIU. On the basis of this superior sensing performance, the FP-lens cavity has the great potential for applications in biosensors.

Abstract Adverse perinatal factors can interfere with the normal development of the brain, potentially resulting in long-term effects on the comprehensive development of children. Presently, the understanding of cognitive and neurodevelopmental processes under conditions of adverse perinatal factors is substantially limited. There is a critical need for an open resource that integrates various perinatal factors with the development of the brain and mental health to facilitate a deeper understanding of these developmental trajectories. In this Data Descriptor, we introduce a multicenter database containing information on perinatal factors that can potentially influence children’s brain-mind development, namely, periCBD, that combines neuroimaging and behavioural phenotypes with perinatal factors at county/region/central district hospitals. PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development associated with perinatal factors among children aged 3–10 years. Ultimately, our goal is to help understand how different adverse perinatal factors specifically impact cognitive development and neurodevelopment. Herein, we provide a systematic overview of the data acquisition/cleaning/quality control/sharing, processes of periCBD.

Segmental bone defects, stemming from trauma, infection, and tumors, pose formidable clinical challenges. Traditional bone repair materials, such as autologous and allogeneic bone grafts, grapple with limitations including source scarcity and immune rejection risks. The advent of nucleic acid nanotechnology, particularly the use of DNA hydrogels in tissue engineering, presents a promising solution, attributed to their biocompatibility, biodegradability, and programmability. However, these hydrogels, typically hindered by high gelation temperatures (∼46 °C) and high construction costs, limit cell encapsulation and broader application. Our research introduces a novel polymer-modified DNA hydrogel, developed using nucleic acid nanotechnology, which gels at a more biocompatible temperature of 37 °C and is cost-effective. This hydrogel then incorporates tetrahedral Framework Nucleic Acid (tFNA) to enhance osteogenic mineralization. Furthermore, considering the modifiability of tFNA, we modified its chains with Aptamer02 (Apt02), an aptamer known to foster angiogenesis. This dual approach significantly accelerates osteogenic differentiation in bone marrow stromal cells (BMSCs) and angiogenesis in human umbilical vein endothelial cells (HUVECs), with cell sequencing confirming their targeting efficacy, respectively. In vivo experiments in rats with critical-size cranial bone defects demonstrate their effectiveness in enhancing new bone formation. This innovation not only offers a viable solution for repairing segmental bone defects but also opens avenues for future advancements in bone organoids construction, marking a significant advancement in tissue engineering and regenerative medicine.

Abstract Mechano-sensitive hair-like sensilla (MSHS) have an ingenious and compact three-dimensional structure and have evolved widely in living organisms to perceive multidirectional mechanical signals. Nearly all MSHS are iontronic or electronic, including their biomimetic counterparts. Here, an all-optical mechano-sensor mimicking MSHS is prototyped and integrated based on a thin-walled glass microbubble as a flexible whispering-gallery-mode resonator. The minimalist integrated device has a good directionality of 32.31 dB in the radial plane of the micro-hair and can detect multidirectional displacements and forces as small as 70 nm and 0.9 μN, respectively. The device can also detect displacements and forces in the axial direction of the micro-hair as small as 2.29 nm and 3.65 μN, respectively, and perceive different vibrations. This mechano-sensor works well as a real-time, directional mechano-sensory whisker in a quadruped cat-type robot, showing its potential for innovative mechano-transduction, artificial perception, and robotics applications.

Abstract Tandem repeat genetic profiles used in forensic applications varies between populations. Despite the diversity and security issues in the Sahel that require the identification of victims (soldiers and civilians), Burkina Faso (BF) remains understudied. To fill this information gap, 396 unrelated individuals from BF were genotyped using a MICROREADER 21 ID System kit. All 20 short tandem repeat (STR) loci tested passed the Hardy–Weinberg equilibrium (HWE) test. The combined powers of exclusion for duos (CPE duos) and trios (CPE trios) for the 20 tested loci were 0.9999998 and 0.9999307, respectively. The probability that two individuals would share the same DNA profiles among the BF population was 9.80898 × 10–26. For the X-chromosome STR analysis, 292 individuals were included in this study using a MICROREADER 19X Direct ID System kit. Among the 19 loci, no significant deviations from HWE test were observed in female samples after Bonferroni correction (p

Abstract Carbon and nitrogen are considered as candidate light elements present in planetary cores. However, there is limited understanding regarding the structure and physical properties of Fe‐C‐N alloys under extreme conditions. Here diamond anvil cell experiments were conducted, revealing the stability of hexagonal‐structured Fe7(N0.75C0.25)3 up to 120 GPa and 2100 K, without undergoing any structural transformation or dissociation. Notably, the thermal expansion coefficient and Grüneisen parameter of the alloy exhibit a collapse at 55–70 GPa. First‐principles calculations suggest that such anomaly is associated with the spin transition of iron within Fe7(N0.75C0.25)3. Our modeling indicates that the presence of ∼1.0 wt% carbon and nitrogen in liquid iron contributes to 9–12% of the density deficit of the Earth's outer core. The thermoelastic anomaly of the Fe‐C‐N alloy across the spin transition is likely to affect the density and seismic velocity profiles of (C,N)‐rich planetary cores, thereby influencing the dynamics of such cores.

Background Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena. Methods Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies. Results The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively. Conclusions This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.

Streptococcus suis (S. suis) is a Gram-positive bacterium and the main culprit behind zoonotic outbreaks, posing a serious threat to public health. The prevalent strains in China are mainly of sequence types (ST) 1 and 7, with few cases of human infections caused by other sequence type being reported. This study presents the first isolation of a ST25 strain from the blood of a septicemic patient. A 57-year-old febrile patient was admitted to a hospital in Hainan of China, diagnosed as septicemia and hepatic dysfunction. A strain of S. suis was isolated from blood culture and confirmed to be serotype 2 and ST25 through 16S rRNA sequencing and whole-genome sequencing, and its genome was further analyzed for gene functions and presence of drug resistance genes. The full-length genome of strain HN28 spans 2,280,124 bp and encodes a total of 2291 proteins. Genes annotated in COG, GO, KEGG, CAZy, and PHl databases accounted for 75.38 %, 69.14 %, 55.35 %, 4.58 %, and 11.87 % of the total predicted proteins, respectively. Virulence factor analysis revealed the presence of seven putative virulence genes in strain HN28. Analysis using the CARD database identified 51 resistance genes in HN28, alongside abundant exocytosis systems. These findings underscore the occurrence of S. suis infections in humans caused by less common ST, emphasizing the need for enhanced epidemiological investigations and monitoring of S. suis infections in the human population.

Medical adhesives play an important role in clinical medicine because of their flexibility and convenient operation. However, they are still limited to laparoscopic surgeries, which have demonstrated urgent demand for liver retraction with minimal damage to the human body. Here, inspired by the suction cup structure of octopus, an adhesive patch with excellent mechanical properties, robust and switchable adhesiveness, and biocompatibility is proposed. The adhesive patch is combined by the attachment body mainly made of poly(acrylic acid) grafted with N-hydroxysuccinimide ester, crosslinked biodegradable gelatin methacrylate and biodegradable biopolymer gelatin to mimic the adhesive sucker rim, and the temperature-sensitive telescopic layer of microgel-crosslinked poly(N-isopropylacrylamide-co-2-hydroxyethyl methacrylate) to shrink and form internal cavity with reduced pressure. Through mechanical tests, adhesion evaluation, and biocompatibility analysis, the bioinspired adhesive patch has demonstrated its capacity not only in adhesion to tissues but also in potential treatment for medical applications, especially laparoscopic technology. The bioinspired adhesive patch can break through the limitations of traditional retraction methods, and become an ideal candidate for liver retraction in laparoscopic surgery and related clinical medicine.

Ischemic stroke rapidly increases the expression level of vascular endothelial growth factor (VEGF), which promotes neovascularization during hypoxia. However, the effect and mechanism of VEGF intervention on cerebrovascular formation remain unclear.Therefore, our research discussed the protective effect of exogenous VEGF on cells in hypoxia environment in cerebral microvascular endothelial cells, simulating ischemic stroke in hypoxic environment. Firstly, we detected the proliferation and apoptosis of cerebral microvascular endothelial cells under hypoxia environment, as well the expression levels of VEGF-E, vascular endothelial growth factor re-ceptor-2 (VEGFR-2), BCL2, PRKCE and PINK1. Moreover, immunofluorescence and western blotting were used to verify the regulation of exogenous VEGF-E on VEGFR-2 expression in hypoxic or normal oxygen environment. Lastly, we manipulated the concentration of VEGF-E in the culture medium to investigate its impact on phospholipase Cγ1 (PLCγ1)/extracellular signaling regulatory protein kinase (ERK) -1/2 and protein kinase B (AKT) pathways. Additionally, we employed a PLCγ1 inhibitor (U73122) to investigate its impact on proliferation and PLCγ1/ERK pathways.The results show that hypoxia inhibited the proliferation of cerebral microvascular endothelial cells, promoted cell apoptosis, significantly up-regulated the expression of VEGF-E, VEGFR-2, PRKCE and PINK1, but down-regulated the expression of BCL2. Interference from exogenous VEGF-E activated PLCγ1/ERK-1/2 and AKT pathways, promoting cell proliferation and inhibiting apoptosis of hypoxic brain microvascular endothelial cells.In summary, exogenous VEGF-E prevents hypoxia-induced damage to cerebral microvascular endothelial cells by activating the PLCγ1/ERK and AKT pathways. This action inhibits the apoptosis pathway in hypoxic cerebral microvascular endothelial cells, thereby safeguarding the blood-brain barrier and the nervous system.

Fractured soft and low permeability coalbed is commonly developed in China, which restricts the increase of single well gas production and industrial development of coalbed methane (CBM). Indirect fracturing is a kind of fracturing method to communicate with coal seams by creating vertical fractures in the neighboring layers and thus achieve effective coal seam transformation, which can solve disadvantages such as drilling collapse, coal dust production, fracturing fluid filtration loss and thin coal seam thickness. In this paper, physical simulation experiments and extended finite element numerical simulation analysis of indirect fracturing are used to reveal the factors influencing the fracture expansion of indirect fracturing in the coal seam roof and to clarify the fracture expansion mechanism in order to provide guidance for indirect fracturing technology. The indirect fracturing experiments under the influence of direct fracturing of coal seam and different fracture initiation location, vertical stress and construction displacement show that high fracture initiation pressure is more likely to produce long fractures, and the influence of primary fractures is significantly reduced; however, the farther the fracture initiation point is from the coal seam, the more energy is required for fracture initiation, and high fracture pressure will cause crushing damage to the coal seam; under large construction displacement, the fracture initiation pressure is correspondingly increased, the fracture initiation time becomes shorter, and the influence of primary fractures becomes smaller. The degree of impact of primary fracture becomes smaller. Under the condition of model parameter setting in this paper，the numerical simulation results considering the parameters of ground stress, fracture initiation location, rock mechanics and construction displacement show that the maximum horizontal principal stress and vertical stress difference is 3 MPa, and the combination of coal and roof elastic modulus difference

Knee osteoarthritis (OA) is a prevalent musculoskeletal condition affecting millions worldwide, posing significant health and economic burdens. Characterized by the degeneration of joint cartilage, the progression of knee OA varies significantly among individuals, making its prediction a complex issue. Previous studies on automated knee OA diagnosis have primarily relied on unimodal data, often overlooking the valuable information present in multi-modal data. Multi-modal learning, which integrates information from various modalities, is increasingly recognized for its potential to enhance diagnostic performance in medical applications. However, such models incur a higher computational load due to the additional data required. This research investigates the feasibility of multi-modal neural networks in knee OA diagnosis by integrating structural demographic data with unstructured imaging data. Three deep learning unimodal models (InceptionV3, DIKO, and EfficientNetv2) were transformed into multi-modal architectures (MF_InceptionNet, MF_DIKO, and MF_Eff) to compare their diagnostic capabilities. The proposed multi-modal models share a common architecture, with unimodal models acting as image feature extraction backbones and separate embedding layers for demographic data. The image features and demographic embeddings are combined into a unified vector before classification. Extensive experiments were conducted to evaluate the performance of these models across different class categories and dataset sizes. MF_DIKO and InceptionV3 emerged as the best multi-modal and unimodal neural networks, respectively, with overall accuracies of 0.67 and 0.75 for 3-class severity classification. Contrary to existing literature, our findings reveal that unimodal neural networks using only imaging features outperform multi-modal networks, suggesting unimodal models might suffice in certain applications.

Diabetic Retinopathy (DR) is the most common complication of Diabetes Mellitus and can lead to blindness if not detected early. Since DR is often asymptomatic in its early stage, timely diagnosis is crucial. Artificial Intelligence (AI) has the potential to facilitate early disease detection and treatment, but its implementation in the medical field raises significant privacy concerns. The sensitive nature of healthcare data, which includes personal information and medical history, makes data privacy a critical issue. This paper explores the implementation of AI models to predict DR risks while incorporating common defense algorithms to enhance data privacy. An unstructured dataset, specifically the DDR dataset, was used to train Deep Learning (DL) models. Two families of DL models, ResNets and DenseNets, were trained and evaluated based on the performance metrics. ResNet 50 and DenseNet 169 demonstrated superior performance and were selected for further privacy enhancement using encryption. The results indicated that privacy-preserving methods, particularly encryption, did not significantly impact the model performance. In summary, this paper highlights the potential of privacy-preserving AI in predicting the risks of DR.

Early detection of knee osteoarthritis is crucial because the damage in the knee joint is irreversible at the advanced stage. Medical images such as Magnetic Resonance Imaging plays an important role in knee osteoarthritis diagnosis as it provides excellent visualization of the osteoarthritis imaging biomarkers. Current clinical practice relies on manual inspection of the images which is very tedious, especially for 3D volumetric data. The overall aim of the study is to develop an efficient fully automated 3D segmentation model for segmenting multiple knee joint tissues from 3D Magnetic Resonance Imaging volumes. This study contributes by implementing hyperparameter optimization techniques to develop the optimal model for knee segmentation which will be beneficial for the detection of knee osteoarthritis. The model employs depthwise separable convolution for better computational efficiency. This paper presents an efficient model for knee bones and cartilages segmentation, modelled by Tree-of-Parzen-Estimators algorithm, which achieved an average dice score of 0.939 and a Jaccard index of 0.891. Our model outperformed 3D U-Net and 3D V-Net by approximately 7% and 6% respectively in terms of Dice Similarity Coefficient, with remarkably less computations, using the same dataset. The efficient model enhances the segmentation of the knee structures for better visualization, which contributes to a more accurate diagnosis in clinical practice. It also reduces the computational cost, allowing more possible adaptation of 3D neural networks in real-world clinical settings. Therefore, this work contributes to advance medical imaging and diagnostics while also holds the potential to improve clinical practice.

Astragali Radix, a traditional Chinese herbal medicine widely used for its medicinal properties, is known to be rich in active components that possess various pharmacological effects. However, the effectiveness of microbial fermentation in enhancing the content of these active substances remains unclear. In this study, a microflora of lactic acid bacteria was used to ferment Astragali Radix, and the promoting effect of Chlorella Growth Factor (CGF) on the fermentation process was investigated so as to clarify the changes in major active compound content in the fermented Astragali Radix broth. Non-targeted metabolomic analysis based on ultra-high-performance liquid chromatography–mass spectrometry was conducted to analyze the differences in metabolites before and after fermentation. The results showed that the total polysaccharide, total flavonoid, and total saponin content in the fermented Astragali Radix broth increased by up to 51.42%, 97.76%, and 72.81% under the optimized conditions, respectively. Streptococcus lutetiensis was the dominant bacterial species during the fermentation process. There were significant differences in metabolites in the fermentation broth before and after fermentation, among which amino acids (such as L-Aspartyl-L-Phenylalanine, etc.) and saponin compounds (such as Cloversaponin I, Goyasaponin I, etc.) were the main upregulated metabolites, which can enhance the physiological functions of Astragali Radix fermentation broth. The CGF exhibited the ability to promote the increase of active substance content in the fermented Astragali Radix broth.

Background Breast cancer remains a pressing global health concern, necessitating accurate diagnostics for effective interventions. Deep learning models (AlexNet, ResNet-50, VGG16, GoogLeNet) show remarkable microcalcification identification (>90%). However, distinct architectures and methodologies pose challenges. We propose an ensemble model, merging unique perspectives, enhancing precision, and understanding critical factors for breast cancer intervention. Evaluation favors GoogleNet and ResNet-50, driving their selection for combined functionalities, ensuring improved precision, and dependability in microcalcification detection in clinical settings. Methods This study presents a comprehensive mammogram preprocessing framework using an optimized deep learning ensemble approach. The proposed framework begins with artifact removal using Otsu Segmentation and morphological operation. Subsequent steps include image resizing, adaptive median filtering, and deep convolutional neural network (D-CNN) development via transfer learning with ResNet-50 model. Hyperparameters are optimized, and ensemble optimization (AlexNet, GoogLeNet, VGG16, ResNet-50) are constructed to identify the localized area of microcalcification. Rigorous evaluation protocol validates the efficacy of individual models, culminating in the ensemble model demonstrating superior predictive accuracy. Results Based on our analysis, the proposed ensemble model exhibited exceptional performance in the classification of microcalcifications. This was evidenced by the model’s average confidence score, which indicated a high degree of dependability and certainty in differentiating these critical characteristics. The proposed model demonstrated a noteworthy average confidence level of 0.9305 in the classification of microcalcification, outperforming alternative models and providing substantial insights into the dependability of the model. The average confidence of the ensemble model in classifying normal cases was 0.8859, which strengthened the model’s consistent and dependable predictions. In addition, the ensemble models attained remarkably high performances in terms of accuracy, precision, recall, F1-score, and area under the curve (AUC). Conclusion The proposed model’s thorough dataset integration and focus on average confidence ratings within classes improve clinical diagnosis accuracy and effectiveness for breast cancer. This study introduces a novel methodology that takes advantage of an ensemble model and rigorous evaluation standards to substantially improve the accuracy and dependability of breast cancer diagnostics, specifically in the detection of microcalcifications.

Background Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies’ flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

Accurate extraction of pores and fractures is a prerequisite for constructing digital rocks for physical property simulation and microstructural response analysis. However, fractures in CT images are similar in grayscale to the rock matrix, and traditional algorithms have difficulty to achieve accurate segmentation results. In this study, a dataset containing multiscale fracture information was constructed, and a U-Net semantic segmentation model with a scSE attention mechanism was used to classify shale CT images at the pixel level and compare the results with traditional methods. The results showed that the CLAHE algorithm effectively removed noise and enhanced the fracture information in the dark parts, which is beneficial for further fracture extraction. The Canny edge detection algorithm had significant false positives and failed to recognize the internal information of the fractures. The Otsu algorithm only extracted fractures with a significant difference from the background and was not sensitive enough for fine fractures. The MEF algorithm enhanced the edge information of the fractures and was also sensitive to fine fractures, but it overestimated the aperture of the fractures. The U-Net was able to identify almost all fractures with good continuity, with an MIou and Recall of 0.80 and 0.82, respectively. As the image resolution increases, more fine fracture information can be extracted.

Abstract Early spring cold spells can lead to leaf chlorosis during the rice seedling greening process. However, the physiological and molecular mechanisms underlying the rice greening process under low-temperature conditions remain unknown. In this study, comparative transcriptome and morphophysiological analyses were performed to investigate the mechanisms mediating the responses of the Koshihikari (Kos) and Kasalath (Kas) rice cultivars to chilling stress. According to their growth-related traits, electrolyte leakage, and chlorophyll fluorescence parameters, Kos was more tolerant to low-temperature stress than Kas. Moreover, chloroplast morphology was more normal (e.g., oval) in Kos than in Kas at 17 °C. The comparative transcriptome analysis revealed 610 up-regulated differentially expressed genes that were common to all four comparisons. Furthermore, carotenoid biosynthesis was identified as a critical pathway for the Kos response to chilling stress. The genes in the carotenoid biosynthesis pathway were expressed at higher levels in Kos than in Kas at 17 °C, which was in accordance with the higher leaf carotenoid content in Kos than in Kas. The lycopene β-cyclase and lycopene ε-cyclase activities increased more in Kos than in Kas. Additionally, the increases in the violaxanthin de-epoxidase and carotenoid hydroxylase activities in Kos seedlings resulted in the accumulation of zeaxanthin and lutein and mitigated the effects of chilling stress on chloroplasts. These findings have clarified the molecular mechanisms underlying the chilling tolerance of rice seedlings during the greening process.

As an emerging secondary battery system, aqueous zinc-ion batteries (AZIBs) show a broad application prospect in the fields of large-scale energy storage and wearable devices. Manganese-based cathode materials have been widely investigated by many researchers due to their high natural abundance, low toxicity, and multiple variable valence states. However, limited active sites, insufficient solvation, and reactivity kinetics of Mn2+ lead to the attenuation of their electrochemical performance. Herein, we introduce appropriate oxygen vacancies into the δ-MnO2 structure by modulating the annealing temperature. The obtained δ-MnO2-400 electrode provided 503 mAh/g capacity at 0.2 A/g and 99% capacity retention after 3000 times cycling at 1 A/g.

True morels (Morchella spp.) are renowned for their aroma and taste, and hot air drying is widely used to extend the shelf life of harvested morels. However, the effects of different drying temperatures on volatile flavor compounds and non-volatile metabolites in the morel are poorly understood. Here, fresh morels (Morchella sextelata) were air-dried at low (45 °C, LT), medium (55 °C, MT), and high temperatures (65 °C, HT). The volatile flavor compounds and non-volatile metabolites were analyzed using GC-IMS and LC-MS/MS, respectively. The GC-IMS revealed that aldehydes, hydrocarbons, and pyrazines increased at greater temperatures, while acids, alcohols, and esters decreased. Random forest machine learning indicated that 1-hexanol and ethyl 3-methylbutanoate were indicative flavor compounds at LTs, while those at MTs and HTs were hexanal and valeraldehyde, respectively. Greater temperatures reduced acetic acid, an unpleasant sour flavor. The LC-MS/MS showed that the relative abundance of amino acids and nucleotides increased with the temperature, with the same trend in 5′-nucleotides and flavor amino acids. Sorbitol 6-phosphate was indicative of the non-volatile metabolites at LTs, while several amino acids were indicative at MTs and HTs. This study revealed the flavor and taste characteristics of morels dried at different temperatures, providing a theoretical reference for establishing a standardized postharvest morel drying process and maintaining morel quality.

Additive manufacturing (AM) is a technology that builds parts layer by layer. Over the past decade, metal additive manufacturing (AM) technology has developed rapidly to form a complete industry chain. AM metal parts are employed in a multitude of industries, including biomedical, aerospace, automotive, marine, and offshore. The design of components can be improved to a greater extent than is possible with existing manufacturing processes, which can result in a significant enhancement of performance. Studies on the anisotropy of additively manufactured metallic materials have been reported, and they describe the advantages and disadvantages of preparing different metallic materials using additive manufacturing processes; however, there are few in-depth and comprehensive studies that summarize the microstructural and mechanical properties of different types of additively manufactured metallic materials in the same article. This paper begins by outlining the intricate relationship between the additive manufacturing process, microstructure, and metal properties. It then explains the fundamental principles of powder bed fusion (PBF) and directed energy deposition (DED). It goes on to describe the molten pool and heat-affected zone in the additive manufacturing process and analyzes their effects on the microstructure of the formed parts. Subsequently, the mechanical properties and typical microstructures of additively manufactured titanium alloys, stainless steel, magnesium–aluminum alloys, and high-temperature alloys, along with their anisotropy, are summarized and presented. The summary indicates that the factors leading to the anisotropy of the mechanical properties of metallic AM parts are either their unique microstructural features or manufacturing defects. This anisotropy can be improved by post-heat treatment. Finally, the most recent research on the subject of metal AM anisotropy is presented.

The development of fractures under multiple geological tectonic movements affects the occurrence and efficient production of free gas in deep coal reservoirs. Taking the No.8 deep coal seam of the Benxi formation in the Linxing area as the object, a method for evaluating favorable fracture areas is established based on the combination of field joint staging, paleogeological model reconstruction under structural leveling, finite element numerical simulation, and fracture development criteria. The results show that a large number of shear fractures and fewer tensile joints are developed in the Benxi formation in the field and mainly formed in the Yanshanian and Himalayan periods. The dominant strikes of conjugate joints in the Yanshanian period are NWW (100°~140°) and NNW (150°~175°), with the maximum principal stress magnitude being 160 MPa along the NW orientation. Those in the Himalayan period are in the NNE direction (0°~40°) and the EW direction (80°~110°), with the maximum principal stress magnitude being 100 MPa along the NE orientation. The magnitudes of the maximum principal stress of the No. 8 deep coal seam in the Yanshanian period are between −55 and −82 MPa, indicative of compression; those in the Himalayan period are from −34 to −70 MPa in the compressive stress form. Areas with high shear stress values are mainly distributed in the central magmatic rock uplift, indicating the influence of magmatic rock uplift on in situ stress distribution and fracture development. Based on the comprehensive evaluation factors of fractures, the reservoir is divided into five classes and 24 favorable fracture areas. Fractures in Class I areas and Class II areas are relatively well developed and were formed under two periods of tectonic movements. The method for evaluating favorable fracture areas is not only significant for the prediction of fractures and free gas contents in this deep coal reservoir but also has certain reference value for other reservoirs.

Ni3S2 is a kind of transition metal sulfide (TMD) with excellent electrical conductivity and electrochemical activity. To further enhance the specific capacity of Ni3S2-based supercapacitors, we synthesize several nanosheet-decorated Ni3S2@NiMo-LDH nanostructures by a combination of hydrothermal and electrodeposition processes. The mesoporous structure provides a large number of electroactive sites, which shortens the charge transfer distance and increases the specific surface area of electrode materials. The assembled asymmetric supercapacitor shows an energy density of 62.8 W h kg−1 at 2701.6 W kg−1 and long-term cycling stability.

Three-dimensional (3D) single-object tracking (3D SOT) is a fundamental yet not well-solved problem in 3D vision, where the complexity of feature matching and the sparsity of point clouds pose significant challenges. To handle abrupt changes in appearance features and sparse point clouds, we propose a novel 3D SOT network, dubbed CDTracker. It leverages both cosine similarity and an attention mechanism to enhance the robustness of feature matching. By combining similarity embedding and attention assignment, CDTracker performs template and search area feature matching in a coarse-to-fine manner. Additionally, CDTracker addresses the problem of sparse point clouds, which commonly leads to inaccurate tracking. It incorporates relatively dense sampling based on the concept of point cloud segmentation to retain more target points, leading to improved localization accuracy. Extensive experiments on both the KITTI and Waymo datasets demonstrate clear improvements in CDTracker over its competitors.

Abstract Background Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is increasing worldwide. Although there is currently no completely curative treatment, helminthic therapy shows certain therapeutic potential for UC. Many studies have found that Trichinella spiralis (T.s) has a protective effect on UC, but the specific mechanism is still unclear. Methods Balb/c mice drank dextran sulfate sodium (DSS) to induce acute colitis and then were treated with T.s. In vitro experiments, the LPS combination with ATP was used to induce the pyroptosis model, followed by intervention with crude protein from T.s (T.s cp). Additionally, the pyroptosis agonist of NSC or the pyroptosis inhibitor vx-765 was added to intervene to explore the role of pyroptosis in DSS-induced acute colitis. The degree of pyroptosis was evaluated by western blot, qPCR and IHC, etc., in vivo and in vitro. Results T.s intervention significantly inhibited NLRP3 inflammasome activation and GSDMD-mediated pyroptosis by downregulating the expression of pyroptosis-related signatures in vitro (cellular inflammatory model) and in vivo (DSS-induced UC mice model). Furthermore, blockade of GSDMD-mediated pyroptosis by the caspase-1 inhibitor vx-765 has a similar therapeutic effect on DSS-induced UC mice with T.s intervention, thus indicating that T.s intervention alleviated DSS-induced UC in mice by inhibiting GSDMD-mediated pyroptosis. Conclusion This study showed that T.s could alleviate the pathological severity UC via GSDMD-mediated pyroptosis, and it provides new insight into the mechanistic study and application of helminths in treating colitis. Graphical Abstract

Abstract The excessive fluctuation of the floating capacitance's (FCs) voltage affects the output performance and output power of the 3P‐5L‐ANPC converter. In order to minimize the fluctuation of the FCs voltage, an optimization control method based on zero‐sequence voltage (ZSV) injection is proposed. The method takes reducing the maximum amplitude of the voltage fluctuation of the three‐phase floating capacitor as the control target. Firstly, the mathematical equation between the modulation voltage and the voltage fluctuation of the FCs are established, and then the maximum fluctuation amplitude of the FCs voltage is obtained by using the established mathematical equations. Secondly, the range of ZSV injection is determined according to the conditions such as the control of neutral point voltage and the limitation of high‐phase voltage leaps. Finally, the equations of the maximum amplitude in the voltage fluctuation of the three‐phase FCs are established, and the ZSV with the minimum voltage amplitude fluctuation of the FCs is obtained within the effective range of the ZSV injection. Compared with the traditional shifted pulse width modulation (SPWM)/space vector pulse width modulation (SVPWM) method, the proposed method can effectively reduce the fluctuation of the FC voltage by 50%. Simulation and experimental results are presented to verify the effectiveness of the proposed method.

Different head positions affect the responses of the vestibular semicircular canals (SCCs) to angular movement. Specific head positions can relieve vestibular disorders caused by excessive stimulating SCCs. In this study, we quantitatively explored responses of human SCCs using numerical simulations of fluid-structure interaction and vestibulo-ocular reflex (VOR) experiments under different forward-leaning angles of the head, including 0°, 10°, 20°, 30°, 40°, 50°, and 60°. It was found that the horizontal nystagmus slow-phase velocity and corresponding biomechanical responses of the cupula in horizontal SCC increased with the forward-leaning angles of the head, reached a maximum when the head was tilted 30° forward, and then gradually decreased. However, no obvious vertical or torsional nystagmus was observed in the VOR experiments. In the numerical model of bilateral SCCs, the biomechanical responses of the cupula in the left anterior SCC and the right anterior SCC showed the same trends; they decreased with the forward-leaning angles, reached a minimum at a 40° forward tilt of the head, and then gradually increased. Similarly, the biomechanical responses of the cupula in the left posterior SCC and in the right posterior SCC followed a same trend, decreasing with the forward-leaning angles, reaching a minimum at a 30° forward tilt of the head, and then gradually increasing. Additionally, the biomechanical responses of the cupula in both the anterior and posterior SCCs consistently remained lower than those observed in the horizontal SCCs across all measured head positions. The occurrence of these numerical results was attributed to the consistent maintenance of mutual symmetry in the bilateral SCCs with respect to the mid-sagittal plane containing the axis of rotation. This symmetry affected the distribution of endolymph pressure, resulting in biomechanical responses of the cupula in each pair of symmetrical SCCs exhibiting same tendencies under different forward-leaning angles of the head. These results provided a reliable numerical basis for future research to relieve vestibular diseases induced by spatial orientation of SCCs.

ABSTRACTChlamydia, an obligate intracellular bacterial pathogen, has a unique developmental cycle involving the differentiation of invading elementary bodies (EBs) to noninfectious reticulate bodies (RBs), replication of RBs, and redifferentiation of RBs into progeny EBs. Progression of this cycle is regulated by three sigma factors, which direct the RNA polymerase to their respective target gene promoters. We hypothesized that the Chlamydia-specific transcriptional regulator GrgA, previously shown to activate σ66 and σ28, plays an essential role in chlamydial development and growth. To test this hypothesis, we applied a novel genetic tool known as dependence on plasmid-mediated expression to create Chlamydia trachomatis with conditional GrgA deficiency. We show that GrgA-deficient C. trachomatis RBs have a growth rate that is approximately half of the normal rate and fail to transition into progeny EBs. In addition, GrgA-deficient C. trachomatis fails to maintain its virulence plasmid. Results of RNA-Seq analysis indicate that GrgA promotes RB growth by optimizing tRNA synthesis and expression of nutrient-acquisition genes, while it enables RB-to-EB conversion by facilitating the expression of a histone and outer membrane proteins required for EB morphogenesis. GrgA also regulates numerous other late genes required for host cell exit and subsequent EB invasion into host cells. Importantly, GrgA stimulates the expression of σ54, the third and last sigma factor, and its activator, AtoC, and thereby indirectly upregulating the expression of σ54-dependent genes. In conclusion, our work demonstrates that GrgA is a master transcriptional regulator in Chlamydia and plays multiple essential roles in chlamydial pathogenicity.IMPORTANCEHallmarks of the developmental cycle of the obligate intracellular pathogenic bacterium Chlamydia are the primary differentiation of the infectious elementary body (EB) into the proliferative reticulate body (RB) and the secondary differentiation of RBs back into EBs. The mechanisms regulating these transitions remain unclear. In this report, we developed an effective novel strategy termed dependence on plasmid-mediated expression (DOPE) that allows for the knockdown of essential genes in Chlamydia. We demonstrate that GrgA, a Chlamydia-specific transcription factor, is essential for the secondary differentiation and optimal growth of RBs. We also show that GrgA, a chromosome-encoded regulatory protein, controls the maintenance of the chlamydial virulence plasmid. Transcriptomic analysis further indicates that GrgA functions as a critical regulator of all three sigma factors that recognize different promoter sets at developmental stages. The DOPE strategy outlined here should provide a valuable tool for future studies examining chlamydial growth, development, and pathogenicity.

Circular RNA (circRNA) has been confirmed to play a vital role in pancreatic ductal adenocarcinoma (PDAC) progression. However, the function and regulatory mechanism of hsa_circ_0012634 in PDAC progression remain unclear. Quantitative real-time PCR was used to measure the expression of hsa_circ_0012634, microRNA (miR)-147b and homeodomain interacting protein kinase 2 (HIPK2). Cell function was assessed by cell counting kit 8 assay, EdU assay, colony formation assay and flow cytometry. Glucose uptake and lactate production were evaluated to determine cell glycolysis ability. Protein expression was examined by western blot analysis. RNA interaction was confirmed by RNA pull-down assay and dual-luciferase reporter assay. Exosomes were isolated from serums and cell culture supernatant using ultracentrifugation and identified by transmission electron microscopy. Animal experiments were conducted using nude mice. Hsa_circ_0012634 was downregulated in PDAC tissues and cells, and its overexpression suppressed PDAC cell proliferation, glycolysis and enhanced apoptosis. MiR-147b was targeted by hsa_circ_0012634, and its inhibitors repressed PDAC cell growth and glycolysis. HIPK2 could be targeted by miR-147b, and hsa_circ_0012634 regulated miR-147b/HIPK2 to suppress PDAC cell progression. Hsa_circ_0012634 was lowly expressed in serum exosomes of PDAC patients. Exosomal hsa_circ_0012634 inhibited PDAC cell growth and glycolysis in vitro, as well as tumorigenesis in vivo. Exosomal hsa_circ_0012634 restrained PDAC progression via the miR-147b/HIPK2 pathway, confirming that hsa_circ_0012634 might serve as a diagnosis and treatment biomarker for PDAC.

Natural gas hydrate is a kind of clean energy with great development potential but is still not commercially developed due to the bottlenecks such as exploitation technology, economical efficiency, and environmental effects.In recent years, people have explored the application of hydrate technology in the field of CO2 capture, seawater desalination, energy storage, gas separation, etc. One of the most challenging and critical problems is how the hydrates are formed and decomposed at any time. This paper summarizes the fundamental research on hydrate decomposition dynamics, including hydrate decomposition properties, influencing factors, and dissociation mechanisms. Moreover, the paper reviews the development of hydrate dissociation dynamics models. The existing models are divided into four categories according to dissociation mechanisms: Thermal dissociation models, intrinsic dynamics models, mass transfer dissociation models and integrated models, and their assumptions, main understanding and limitations are highlighted. Future directions for improving hydrate dissociation dynamics research are foreseen to deepen the understanding of hydrate dissociation dynamics and promote the development and utilization of hydrates.

Abstract Aiming at the problems of complex realization, single control target and difficulty in selecting the optimal zero sequence voltage when the traditional space vector PWM (SVPWM) algorithm is adopted in five‐level active neutral‐point‐clamp (5L‐ANPC) converter, a predictive control optimal switching sequence selection (PC‐OSSS) method is proposed. The method adopts the SVPWM control strategy base on the line voltage coordinate and then realizes the selection of optimal switching sequence through the multi‐objective predictive control method. Firstly, the principle of SVPWM based on line voltage coordinate is introduced, and all switching sequences that can be output are obtained. Secondly, the mathematical models of the floating capacitor (FC) voltage, the neutral point (NP) voltage and the common mode (CM) voltage corresponding to each switch sequence are established. Finally, the optimal switch sequence is selected by using the predictive control method based on priority ranking. Compared with traditional SVPWM, the outstanding advantage of this method is that the traditional qualitative comparison method is replaced by quantitative calculation. The multi‐objective control performance of the converter is improved while retaining the advantages of the traditional SVPWM fixed frequency. Finally, the practicability and effectiveness of the proposed control strategy are verified by simulation and experiment.

Mining the core value of Industrial Internet of Things (IIoT) data safely and reducing the risk of malicious attacks are the inherent requirements of industrial data visualization. Visualization technology has become the main tool for data aggregation, mining and analysis of IIoT data through graphical representation. However, visualization technology still has two shortcomings in big data calculation and analysis scenarios. On the one hand, visual results will lead to the disclosure of sensitive privacy. On the other hand, most visualization tools can't provide an interactive framework for users to select the suitable solutions. To address these problems, we present an open accessible Visual framework based on Differential Privacy theory (VisDP), which provides Multi-index Quantitative comprehensive Evaluation technology (MQE) for data mining results. Considering the advantages of interactive mechanism, VisDP provides rich optional schemes, including the operating web, calling API and the downloading SDK. Finally, we verify the availability and privacy of MQE through mathematical proofs, analyze the hospital medical waste detection system that actually applies the framework, and the experimental results have showed the effectiveness and practicality of the proposed platform.

To balance the relationship between current THD and switching loss, an optimal predictive control method using single and double vector hybrid modulation of NPC-3L inverter is proposed. Firstly, the relationship of the THD of output current and the Aoc&rc(the Area enclosed by output current and reference current) is built. Secondly, the current threshold is defined to control the switching time of single/double vectors, which is used to balance the current THD and switching loss and determined by the target current THD value. Thirdly, the output of single vector or double vector is determined by comparing the output real-time current value with the set current threshold. That is, when the output current is greater than the current threshold, a single vector is used to reduce the switching loss. When the output current is less than the current threshold, the THD of the output current is raised by using output double vectors. At the same time, by using this control method, the disadvantages of single/dual vector independent operation are mutually offset. Finally, a simulation model is built to verify the proposed control method. Compared with traditional methods, the simulation results show that the proposed method is correct and effective.

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems.

This study investigates the asymptotic synchronization in fractional memristive neural networks of the Riemann–Liouville type, considering mixed time delays and jump mismatches. Addressing the challenges associated with discrepancies in the circuit switching speed and the accuracy of the memristor, this paper introduces an enhanced model that effectively navigates these complexities. We propose two novel event-based hybrid impulsive controllers, each characterized by unique triggering conditions. Utilizing advanced techniques in inequality and hybrid impulsive control, we establish the conditions necessary for achieving synchronization through innovative Lyapunov functions. Importantly, the developed controllers are theoretically optimized to minimize control costs, an essential consideration for their practical deployment. Finally, the effectiveness of our proposed approach is demonstrated through two illustrative simulation examples.

River water pollution is a key environmental issue to human society. How to effectively simulate the flow velocity and pollution transport in a vegetated river can provide a theoretical basis for solving such problems. Based on previous experimental data, this article uses the lattice Boltzmann method and random displacement method to simulate the velocity distribution and nutrient transport in multi-layer rigid vegetated rivers. The simulation results indicate that incorporating the drag force of the vegetation into the model according to different vegetation layers can effectively simulate the velocity in a multi-layer vegetated river. Incorporating the turbulent diffusion and mechanical diffusion effects of nutrients caused by vegetation into the model can effectively simulate the effects of multi-layer vegetation on nutrient transport. This model can provide effective predictions of the flow velocity and pollution transport in multi-layer vegetation.

Abstract Vanadium‐based compounds with various crystal structures are highly promising cathode materials for aqueous zinc‐based batteries. However, their further development is limited due to the low electrical conductivity, slow zinc ion diffusion, and weak structural stability. It is a feasible strategy to resolve above mentioned issues through surface modification. Herein, we design bi‐phase coexisting CaV2O6/NaV6O15 nanobelt structures with abundant interfaces, which provide more reactive sites than single‐phase ones. The samples as the electrode materials deliver a specific capacity of 312 mAh g−1 at 5 A g−1 after 2000 cycles. They still keep a capacity of 231 mAh g−1 at 10 A g−1 with a cycle life of 6500 times.

Nowadays, with more and more physical education curriculum resources, schools or teachers have more and more choices for physical education curriculum resources. However, because some teachers need a deep understanding of curriculum training programs and standards, the selected curriculum resources cannot promote their curriculum development. This paper puts forward the research on the intelligent recognition and game model of physical education curriculum resources based on neural networks. The specific research conclusions are as follows: The intelligent consciousness and movement model of physical education curriculum resources based entirely on the technical knowledge of the BP neural community and deep neural community are proposed. With the help of MATLAB7.1 neural network toolbox to implement the specific recommendation system, a three-layer BP network is established, and the NEWFF function is used to create the neural network. Useful resources in each direction generate a direction recognition vector according to the route guidance standard, calculate the course recommendation degree according to selection statistics and scoring, and input the course resource recognition vector and recommendation degree into the neural network. When the number of hidden layer nodes is 10, and the learning training algorithm selects the L-M optimization algorithm, the error between the actual output and the expected output of the network meets the requirements. It shows that the accuracy of the recommendation model meets the requirements; that is, the relationship between the recognition vector of physical education course resources and the recommendation degree of course resources reflected by the neural network basically reflects the functional relationship between them and the model can be used to make corresponding recommendations.

Primary colorectal squamous cell carcinoma (CSCC) is a rare pathological subtype. Currently, clinical data with regards to its prognosis and treatment is limited, and there is no optimal treatment method. The case presented involves a proficient mismatch repair (pMMR) and microsatellite-stable (MSS) Colorectal cancer (CRC) patient with squamous cell carcinoma (SCC) located transversely in the colon. Based on the imaging assessment, the tumor infiltration depth is classified as T4. After receiving 4 cycles of neoadjuvant treatment with oxaliplatin and capecitabine (XELOX), the patients were evaluated for partial response (PR) in 2 cycles and stable disease (SD) in 4 cycles. The patient underwent a right hemicolectomy and received postoperative paclitaxel/cisplatin (TC) adjuvant chemotherapy. After 23 months, a systemic examination revealed abdominal metastasis. A needle biopsy was conducted on the detected abdominal metastases, with the resulting pathology indicating the presence of metastatic SCC. The individual exhibited expression of programmed cell death ligand 1 (PD-L1) and a mutation in the TP53 gene. Considering the patient’s disease recurrence based on medical history, a treatment plan was formulated. This involved Sintilimab plus Cetuximab and the combination of leucovorin, fluorouracil, and irinotecan (FOLFIRI) regimen. The patient received four cycles of treatment with an efficacy evaluation of SD- and seven cycles of treatment with an efficacy evaluation of SD+, which resulted in a progression-free survival (PFS) duration of 7 months. This case study presents the conventional XELOX chemotherapy protocol, which has shown limited effectiveness, and highlights the favorable results achieved by implementing the TC adjuvant chemotherapy regimen in individuals diagnosed with primary colonic SCC. Furthermore, combining immune checkpoint blockade (ICB) with other therapies for patients with advanced disease is anticipated to provide an extended duration of survival.

Abstract Background Polymerase chain reaction (PCR) assays are perceived to facilitate the diagnosis of fungal infections. However, due to lack of standardization, the value of bronchoalveolar lavage (BAL) fluid PCR in diagnosis of invasive pulmonary aspergillosis (IPA) remains unclear. Methods We conducted a systematic meta-analysis to evaluate the accuracy of BAL fluid PCR in IPA diagnosis among high-risk patients. All studies involving patients at risk for IPA were included. The sensitivity, specificity, positive and negative likelihood ratios of BAL fluid PCR were summarized for diagnosis of proven/probable IPA, or proven IPA only. Potential heterogeneity was assessed by subgroup analyses and meta-regression. Results Forty-one studies involving 5668 patients were analyzed. The summary sensitivity, specificity, positive and negative likelihood ratios of BAL fluid PCR for proven/probable IPA were 0.75 (95% CI = 0.67–0.81), 0.94 (95% CI = 0.90–0.96), 11.8 (95% CI = 7.7–18.1) and 0.27 (95% CI = 0.20–0.36), respectively. Whereas for proven IPA only, sensitivity and specificity were 0.91 (95% CI = 0.68–0.98) and 0.80 (95% CI = 0.74–0.85) in fourteen studies involving 2061 patients. Significant heterogeneity was present due to the underlying disease, antifungal treatment and differences in DNA extraction techniques and choice of PCR assay. Compared to patients with hematological malignancies (HM) and hematopoietic stem cell/solid organ transplantation (HSCT/SOT), sensitivity was higher in the population with disease such as chronic obstructive pulmonary disease, solid tumor, autoimmune disease with prolonged use of corticosteroids, etc. (0.88 vs. 0.68, P

As a result of the influence of individual appearance and lighting conditions, aberrant noise spots cause significant mis-segmentation for frontal portraits. This paper presents an accurate portrait segmentation approach based on a combination of wavelet proportional shrinkage and an upgraded sparrow search (SSA) clustering algorithm to solve the accuracy challenge of segmentation for frontal portraits. The brightness component of the human portrait in HSV space is first subjected to wavelet scaling denoising. The elite inverse learning approach and adaptive weighting factor are then implemented to optimize the initial center location of the K-Means algorithm to improve the initial distribution and accelerate the convergence speed of SSA population members. The pixel segmentation accuracy of the proposed method is approximately 70% and 15% higher than two comparable traditional methods, while the similarity of color image features is approximately 10% higher. Experiments show that the proposed method has achieved a high level of accuracy in capricious lighting conditions.