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Abstract Objectives This study examined the potential association between nucleated red blood cell (NRBC) levels and mortality in critically ill patients with acute pancreatitis (AP) in the intensive care unit, due to limited existing research on this correlation. Methods This retrospective cohort study utilized data from the MIMIC-IV v2.0 and MIMIC-III v1.4 databases to investigate the potential relationship between NRBC levels and patient outcomes. The study employed restricted cubic splines (RCS) regression analysis to explore non-linear associations. The impact of NRBC on prognosis was assessed using a generalized linear model (GLM) with a logit link, adjusted for potential confounders. Furthermore, four machine learning models, including Gradient Boosting Classifier (GBC), Random Forest, Gaussian Naive Bayes, and Decision Tree Classifier model, were constructed using NRBC data to generate risk scores and evaluate the potential of NRBC in predicting patient prognosis. Results A total of 354 patients were enrolled in the study, with 162 (45.8%) individuals aged 60 years or older and 204 (57.6%) males. RCS regression analysis demonstrated a non-linear relationship between NRBC levels and 90-day mortality. Receiver Operating Characteristic (ROC) analysis identified a 1.7% NRBC cutoff to distinguish survivor from non-survivor patients for 90-day mortality, yielding an Area Under the Curve (AUC) of 0.599, with a sensitivity of 0.475 and specificity of 0.711. Elevated NRBC levels were associated with increased risks of 90-day mortality in both unadjusted and adjusted models (all Odds Ratios > 1, P

Tissue kallikrein (TK) has emerged as a potential neuroprotective agent in ischemic stroke (IS), yet the optimal timing and mechanisms of TK therapy remain unclear. Here, we established a causal link between lower baseline TK levels and an increased risk of stroke through a retrospective, multicenter cohort study involving 2115 initially non-stroke subjects monitored for 5 years. Sequentially, we observed a notable increase in bradykinin receptor 2 (B2R) levels during the ischemic phase of the IS model, while levels of TK and bradykinin receptor 1 (B1R) remained stable. Intriguingly, both B1R and B2R exhibited a significant elevation 24 h after reperfusion. Further investigations in preclinical models demonstrated that TK supplementation activates the PI3K/AKT signaling pathway via enhanced B2R expression during the ischemic phase, leading to nuclear translocation of Hif-1α. This activation enhances the expression of VEGF and eNOS, thereby fortifying the neurovascular unit. Moreover, it suppresses the activation of the kallikrein-kinin system induced by reperfusion injury, effectively reducing inflammation, ROS production, apoptosis, and endothelial barrier dysfunction. Thus, our findings highlight the significance of TK supplementation during the ischemic phase in attenuating reperfusion-induced injury in IS, providing a mechanistic rationale for determining the optimal timing for TK supplementation therapy.

Abstract The contamination of nitric oxide presents a significant environmental challenge, necessitating the development of efficient photocatalysts for remediation. Conventional heterojunctions encounter obstacles such as large contact barriers, sluggish charge transport, and compromised redox capacity. Here, we introduce an innovative S‐type heterostructure photocatalyst, UiO‐66‐NH2/ZnS(en)0.5, designed specifically to overcome these challenges. The synthesis, employing a unique microwave solvothermal method, strategically aligns the lowest unoccupied molecular orbital of UiO‐66‐NH2 with the highest occupied molecular orbital of ZnS(en)0.5, fostering the formation of a stepped heterojunction. The resulting intimate interface contact generates a built‐in electric field, facilitating charge separation and migration, as evidenced by time‐resolved photoluminescence spectroscopy and photoelectrochemical tests. The abundant active sites in the porous UiO‐66‐NH2 counterpart provide adsorption and activation sites for nitrogen monoxide (NO) oxidation. Performance evaluation reveals exceptional photocatalytic NO removal, achieving 70% efficiency and 99% selectivity toward nitrates under simulated solar illumination. Evidence from X‐ray photoelectron spectroscopy and trapping experiments supports the effectiveness of the S‐type heterostructure, showcasing refined reactive oxygen species, particularly superoxide. Thus, this study introduces a new perspective on advanced NO oxidation and unlocks the potential of S‐scheme heterojunctions to refine reactive oxygen species for NO remediation.

Abstract Background This multicenter observational study aimed to determine whether dyslipidemia or obesity contributes more significantly to unfavorable clinical outcomes in patients experiencing a first-ever ischemic stroke (IS). Methods The study employed a machine learning predictive model to investigate associations among body mass index (BMI), body fat percentage (BFP), high-density lipoprotein (HDL), triglycerides (TG), and total cholesterol (TC) with adverse outcomes in IS patients. Extensive real-world clinical data was utilized, and risk factors significantly linked to adverse outcomes were identified through multivariate analysis, propensity score matching (PSM), and regression discontinuity design (RDD) techniques. Furthermore, these findings were validated via a nationwide multicenter prospective cohort study. Results In the derived cohort, a total of 45,162 patients diagnosed with IS were assessed, with 522 experiencing adverse outcomes. A multifactorial analysis incorporating PSM and RDD methods identified TG (adjusted odds ratio (OR) = 1.110; 95% confidence interval (CI): 1.041–1.183; P

Abstract The neural signals produced by varying electrical stimulation parameters lead to characteristic neural circuit responses. However, the characteristics of neural circuits reconstructed by electrical signals remain poorly understood, which greatly limits the application of such electrical neuromodulation techniques for the treatment of spinal cord injury. Here, we develop a dual electrical stimulation system that combines epidural electrical and muscle stimulation to mimic feedforward and feedback electrical signals in spinal sensorimotor circuits. We demonstrate that a stimulus frequency of 10−20 Hz under dual stimulation conditions is required for structural and functional reconstruction of spinal sensorimotor circuits, which not only activates genes associated with axonal regeneration of motoneurons, but also improves the excitability of spinal neurons. Overall, the results provide insights into neural signal decoding during spinal sensorimotor circuit reconstruction, suggesting that the combination of epidural electrical and muscle stimulation is a promising method for the treatment of spinal cord injury.

Abstract Background The present study aimed to investigate the correlation between weight status and mortality in mechanically ventilated patients and explore the potential mediators. Methods Three medical centers encompassing 3301 critically ill patients receiving mechanical ventilation were assembled for retrospective analysis to compare mortality across various weight categories of patients using machine learning algorithms. Bioinformatics analysis identified genes exhibiting differential expression among distinct weight categories. A prospective study was then conducted on a distinct cohort of 50 healthy individuals and 193 other mechanically ventilated patients. The expression levels of the genes identified through bioinformatics analysis were quantified through enzyme-linked immunosorbent assay (ELISA). Results The retrospective analysis revealed that overweight individuals had a lower mortality rate than underweight individuals, and body mass index (BMI) was an independent protective factor. Bioinformatics analysis identified matrix metalloproteinase 8 (MMP-8) as a differentially expressed gene between overweight and underweight populations. The results of further prospective studies showed that overweight patients had significantly lower MMP-8 levels than underweight patients ((3.717 (2.628, 4.191) vs. 2.763 (1.923, 3.753), ng/ml, P = 0.002). High MMP-8 levels were associated with increased mortality risk (OR = 4.249, P = 0.005), indicating that elevated level of MMP-8 predicts the mortality risk of underweight patients receiving mechanical ventilation. Conclusions This study provides evidence for a protective effect of obesity in mechanically ventilated patients and highlights the potential role of MMP-8 level as a biomarker for predicting mortality risk in this population.

In this paper, a MEMS piezoresistive ultrathin silicon membrane-based strain sensor is presented. The sensor’s ability to capture an acoustic emission signal is demonstrated using a Hsu–Nielsen source, and shows comparable frequency content to a commercial piezoceramic ultrasonic transducer. To the authors’ knowledge, this makes the developed sensor the first known piezoresistive strain sensor which is capable of recording low-energy acoustic emissions. The improvements to the nondestructive evaluation and structural health monitoring arise from the sensor’s low minimum detectable strain and wide-frequency bandwidth, which are generated from the improved fabrication process that permits crystalline semiconductor membranes and advanced polymers to be co-processed, thus enabling a dual-use application of both acoustic emission and static strain sensing. The sensor’s ability to document quasi-static bending is also demonstrated and compared with an ultrasonic transducer, which provides no significant response. This dual-use application is proposed to effectively combine the uses of both strain and ultrasonic transducer sensor types within one sensor, making it a novel and useful method for nondestructive evaluations. The potential benefits include an enhanced sensitivity, a reduced sensor size, a lower cost, and a reduced instrumentation complexity.

Autophagy has been typically associated with resistance to cancer therapy, and autophagy inhibitors have been explored in cancer. Here, the authors investigate autophagy signatures and their association with drug response in cancer, and find that autophagy induction can actually sensitise cancer cells to therapy.

ADAR (Adenosine Deaminases Acting on RNA) proteins are a group of enzymes that play a vital role in RNA editing by converting adenosine to inosine in RNAs. This process is a frequent post-transcriptional event observed in metazoan transcripts. Recent studies indicate widespread dysregulation of ADAR-mediated RNA editing across many immune-related diseases, such as human cancer. We comprehensively review ADARs’ function as pattern recognizers and their capability to contribute to mediating immune-related pathways. We also highlight the potential role of site-specific RNA editing in maintaining homeostasis and its relationship to various diseases, such as human cancers. More importantly, we summarize the latest cutting-edge computational approaches and data resources for predicting and analyzing RNA editing sites. Lastly, we cover the recent advancement in site-directed ADAR editing tool development. This review presents an up-to-date overview of ADAR-mediated RNA editing, how site-specific RNA editing could potentially impact disease pathology, and how they could be harnessed for therapeutic applications.

Cancer stem-like cells (CSCs) are critically involved in cancer metastasis and chemoresistance, acting as one major obstacle in clinical practice. While accumulating studies have implicated the metabolic reprogramming of CSCs, mitochondrial dynamics in such cells remain poorly understood. Here we pinpointed OPA1hi with mitochondrial fusion as a metabolic feature of human lung CSCs, licensing their stem-like properties. Specifically, human lung CSCs exerted enhanced lipogenesis, inducing OPA1 expression via transcription factor SAM Pointed Domain containing ETS transcription Factor (SPDEF). In consequence, OPA1hi promoted mitochondrial fusion and stemness of CSCs. Such lipogenesishi, SPDEFhi, and OPA1hi metabolic adaptions were verified with primary CSCs from lung cancer patients. Accordingly, blocking lipogenesis and mitochondrial fusion efficiently impeded CSC expansion and growth of organoids derived from patients with lung cancer. Together, lipogenesis regulates mitochondrial dynamics via OPA1 for controlling CSCs in human lung cancer.

Abstract Mutant KRAS is a key driver in colorectal cancer (CRC) and promotes Myc translation and Myc-dependent stress adaptation and proliferation. Here, we report that the combination of two FDA-approved drugs Bortezomib and Everolimus (RAD001) (BR) is highly efficacious against mutant KRAS CRC cells. Mechanistically, the combination, not single agent, rapidly depletes Myc protein, not mRNA, and leads to GCN2- and p-eIF2α-dependent cell death through the activation of extrinsic and intrinsic apoptotic pathways. Cell death is selectively induced in mutant KRAS CRC cells with elevated basal Myc and p-eIF2α and is characterized by CHOP induction and transcriptional signatures in proteotoxicity, oxidative stress, metabolic inhibition, and immune activation. BR-induced p-GCN2/p-eIF2α elevation and cell death are strongly attenuated by MYC knockdown and enhanced by MYC overexpression. The BR combination is efficacious against mutant KRAS patient derived organoids (PDO) and xenografts (PDX) by inducing p-eIF2α/CHOP and cell death. Interestingly, an elevated four-gene (DDIT3, GADD45B, CRYBA4 and HSPA1L) stress signature is linked to shortened overall survival in CRC patients. These data support that Myc-dependent stress adaptation drives the progression of mutant KRAS CRC and serves as a therapeutic vulnerability, which can be targeted using dual translational inhibitors.

Smoke is more observable than open fires. Optical satellite video has the advantages of a wide monitoring range, fast response speed, and good economy in large-scale surface smoke monitoring tasks. It can be used in wide-area forest wildfire monitoring, battlefield dynamic monitoring, disaster relief decision-making. The smoke segmentation method based on traditional handcrafted features is easily limited by the scene and data. This article introduces the deep learning method to the optical satellite video smoke target segmentation. However, due to the lack of real smoke images and the blurred edges of smoke, there are currently few labeled datasets for smoke segmentation in high-resolution optical satellite imagery scenes, which cannot provide sufficient training data for deep learning models. The smoke image from the satellite perspective also has the characteristics of multiscale features and ground object background interference. To solve the abovementioned problems, we construct a set of high-resolution optical satellite imagery smoke synthesis datasets based on the optical imaging process of smoke targets, which saves the cost of manual labeling. In addition, we design an attention-guided optical satellite video smoke segmentation network model, which can effectively suppress the ground object background's false alarm and extract the smoke's multiscale features. Synthetic data faces the transferability problem in real-world applications, so the physical constraints of the smoke imaging process are introduced into the loss function to improve the generalization of the model in real smoke data. The comprehensive evaluation results show that the method outperforms representative semantic segmentation networks.

This paper investigates the challenging fault prediction problem in process industries that adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the emerging developments of industrial internet of things (IIoT) and Industry 4.0. Particularly, we developed an end-to-end deep learning approach based on a large volume of real-time sensory data collected from a chemical plant equipped with wireless sensors. Firstly, a novel recursive architecture with multi-lookback inputs is proposed to perform autoregression on imbalanced time-series data as a preliminary prediction. In this process, a novel learning algorithm named recursive gradient descent (RGD) is developed for the proposed architecture to reduce cumulative prediction uncertainties. Subsequently, a classification model based on temporal convolutions over multiple channels with decay effect is proposed to perform multi-class classification for fault root cause identification and localization. The overall network is named the cumulative uncertainty reduction network (CURNet), for its superior capacity in reducing prediction uncertainties accumulated over multiple prediction steps. Performance evaluations show that CURNet is able to achieve superior performance especially in terms of fault prediction recall and fault type classification accuracy, compared to the existing techniques.

IntroductionBiofuel is a kind of sustainable, renewable and environment friendly energy. Lignocellulose from the stems of woody plants is the main raw material for “second generation biofuels”. Lignin content limits fermentation yield and is therefore a major obstacle in biofuel production. Plant laccase plays an important role in the final step of lignin formation, which provides a new strategy for us to obtain ideal biofuels by regulating the expression of laccase genes to directly gain the desired lignin content or change the composition of lignin.MethodsMultiple sequence alignment and phylogenetic analysis were used to classify PtrLAC genes; sequence features of PtrLACs were revealed by gene structure and motif composition analysis; gene duplication, interspecific collinearity and Ka/Ks analysis were conducted to identify ancient PtrLACs; expression levels of PtrLAC genes were measured by RNA-Seq data and qRT-PCR; domain analysis combine with cis-acting elements prediction together showed the potential function of PtrLACs. Furthermore, Alphafold2 was used to simulate laccase 3D structures, proLAC23::LAC23-eGFP transgenic Populus stem transects were applied to fluorescence observation.ResultsA comprehensive analysis of the P. trichocarpa laccase gene (PtLAC) family was performed. Some ancient PtrLAC genes such as PtrLAC25, PtrLAC19 and PtrLAC41 were identified. Gene structure and distribution of conserved motifs clearly showed sequence characteristics of each PtrLAC. Combining published RNA-Seq data and qRT-PCR analysis, we revealed the expression pattern of PtrLAC gene family. Prediction results of cis-acting elements show that PtrLAC gene regulation was closely related to light. Through above analyses, we selected 5 laccases and used Alphafold2 to simulate protein 3D structures, results showed that PtrLAC23 may be closely related to the lignification. Fluorescence observation of proLAC23::LAC23-eGFP transgenic Populus stem transects and qRT-PCR results confirmed our hypothesis again.DiscussionIn this study, we fully analyzed the Populus trichocarpa laccase gene family and identified key laccase genes related to lignification. These findings not only provide new insights into the characteristics and functions of Populus laccase, but also give a new understanding of the broad prospects of plant laccase in lignocellulosic biofuel production.

Transdermal drug delivery (TDD) is one of the key approaches for treating diseases, avoiding first-pass effects, reducing systemic adverse drug reactions and improving patient compliance. Microneedling, iontophoresis, electroporation, laser ablation and ultrasound facilitation are often used to improve the efficiency of TDD. Among them, microneedling is a relatively simple and efficient means of drug delivery. Microneedles usually consist of micron-sized needles (50–900 μm in length) in arrays that can successfully penetrate the stratum corneum and deliver drugs in a minimally invasive manner below the stratum corneum without touching the blood vessels and nerves in the dermis, improving patient compliance. Hydrogel-forming microneedles (HFMs) are safe and non-toxic, with no residual matrix material, high drug loading capacity, and controlled drug release, and they are suitable for long-term, multiple drug delivery. This work reviewed the characteristics of the skin structure and TDD, introduced TDD strategies based on HFMs, and summarized the characteristics of HFM TDD systems and the evaluation methods of HFMs as well as the application of HFM drug delivery systems in disease treatment. The HFM drug delivery system has a wide scope for development, but the translation to clinical application still has more challenges.

To overcome the poor water solubility of total flavones of Arisaematis rhizoma, microemulsions (MEs) can be used as a carrier for transdermal administration to promote their solubilization and skin permeability. Here, we investigated the physical compatibility of MEs in hydrogels and their skin permeation-enhancing effects. Transparency of microemulsion-based hydrogels (MBGs) was analyzed to evaluate ME compatibility with different hydrogel matrices. Transmission electron microscopy (TEM) and Fourier transform infrared (FTIR) spectroscopy were used to explore the microstructures of MBGs and ME–hydrogel combinations. Uniform and transparent MBG was obtained by adding 1% sodium hyaluronate (SH) to the optimized ME. MBG prepared with SH as a matrix expressed pseudoplastic-fluid and shear-thinning characteristics, making it easy to apply in clinical settings. No new FTIR peak occurred in the MBG compared with ME and hydrogel matrix, indicating a physical combination of ME and the polymer network gel. Nanoscale droplets of ME migrated in the gel network, and the migration capacity and in vitro transdermal permeation flux negatively correlated with SH concentration in the gel system. In conclusion, in MBGs, ME can keep nanoscale droplets migrating in the hydrogel network, thereby enhancing transdermal drug delivery.

Abstract Emerging evidence has revealed significant roles for small nucleolar RNAs (snoRNAs) in tumorigenesis. However, the genetic and pharmacogenomic landscape of snoRNAs has not been characterized. Using the genotype and snoRNA expression data from The Cancer Genome Atlas, we characterized the effects of genetic variants on snoRNAs across 29 cancer types and further linked related alleles with patient survival as well as genome-wide association study risk loci. Furthermore, we characterized the impact of snoRNA expression on drug response in patients to facilitate the clinical utility of snoRNAs in cancer. We also developed a user-friendly data resource, GPSno ( http://hanlab.uth.edu/GPSno ), with multiple modules for researchers to visualize, browse, and download multi-dimensional data. Our study provides a comprehensive genetic and pharmacogenomic landscape of snoRNAs, which will shed light on future clinical considerations for the development of snoRNA-based targeted therapies.

In cancer, the extracellular matrix is extensively remodeled during chronic inflammation, thus affecting cell transcription, differentiation, migration and cell-cell interactions. Matrix metalloproteinases can degrade the extracellular matrix of tumor tissues and take important roles in disease progression. Numerous efforts to develop cancer treatments targeting matrix metalloproteinases have failed in clinical trials owing to the ineffectiveness and toxicity of the applied inhibitors. In this study, we investigated the potential of targeting matrix metalloproteinases and oncolytic virus combination in cancer therapy. We found that MMP3 expression was upregulated in various cancers and MMP3 expression in the tumor cells, but not in other tissues, was important for tumor growth and metastasis. Single treatment of colon cancer with multiple MMP3 inhibitors was not effective in mice. Nevertheless, the therapeutic effect of MMP3 was greatly improved by combination with an oncolytic virus. A potential mechanism of MMP3 in regulating tumor cell proliferation and invasion was mediated via Erk1/2 an NF-κB signaling. This study reveals that MMP3 is a promising target and the combined treatment with oncolytic virus is a potential strategy for cancer therapy.

Grassland insect diversity in many parts of the world is severely threatened by recent deterioration caused by global change and human activities. Insect species richness and abundance are likely sensitive to grassland degradation because of changes in vegetation structure and microclimates, and yet, our understanding of how diversity of insect community, particularly insect guilds responds to grassland degradation is still limited. Here, we conducted a field experiment to examine the responses of richness and abundance of total insects and different insect guilds along degraded levels (i.e., non-degraded, moderately, and severely degraded site) in meadow steppe at northeastern China. We found that higher species richness of total insects was detected in the moderately degraded sites, and there was no difference in abundance between the three sites. Furthermore, the responses of richness and abundance of each insect guild were significantly different. Hemiptera richness was significantly higher at moderately degraded sites, and Orthoptera richness was higher in severely degraded sites. Abundance of Hemiptera and Orthoptera increased with the increasing levels of degraded grasslands, but the abundance of Hymenoptera and Coleoptera decreased. Moreover, effect size of grassland degradation on Orthoptera abundance was larger from non-degraded to moderately degraded grassland; and larger effect size on Coleoptera abundance from moderately to severely degraded grassland. The different responses of insect guilds to grassland degradation are mainly attributed to changes in food and microclimate availability. In the light of convenience and operability, the finding from the study suggested that the abundance of an insect guild, rather than total insects and insect diversity seem to be a useful ecological indicator when assessing the levels of grassland degradation.

Self-calibration capabilities for flexible pressure sensors are greatly needed for fluid dynamic analysis, structure health monitoring and wearable sensing applications to compensate, in situ and in real time, for sensor drifts, nonlinearity effects, and hysteresis. Currently, very few self-calibrating pressure sensors can be found in the literature, let alone in flexible formats. This paper presents a flexible self-calibrating pressure sensor fabricated from a silicon-on-insulator wafer and bonded on a polyimide substrate. The sensor chip is made of four piezoresistors arranged in a Wheatstone bridge configuration on a pressure-sensitive membrane, integrated with a gold thin film-based reference cavity heater, and two thermistors. With a liquid-to-vapor thermopneumatic actuation system, the sensor can create precise in-cavity pressure for self-calibration. Compared with the previous work related to the single-phase air-only counterpart, testing of this two-phase sensor demonstrated that adding the water liquid-to-vapor phase change can improve the effective range of self-calibration from 3 psi to 9.5 psi without increasing the power consumption of the cavity micro-heater. The calibration time can be further improved to a few seconds with a pulsed heating power.

Grassland degradation is a retrogressive succession of grassland vegetation, which leads to the loss of biodiversity and the degradation of ecosystem functions. Soil microbiomes play critical roles in the functioning and services of grassland ecosystems, yet little is known about how their diversity, structure and co-occurrence network characteristics respond to grassland degradation. Here, we used lllumina Miseq technique to evaluate soil bacterial and fungal communities in a meadow steppe with different degrees of degradation in Northeastern China. Our results showed that Actinobacteria, Proteobacteria, and Chloroflexi and Acidobacteria were the dominant bacterial phyla, while Ascomycota, Basidiomycota, and Zygomycota were the predominant fungal phyla. The relative abundance of taxa assigned to Actinobacteria, Gemmatimonadetes, Firmicutes, and Deinococcus-Thermus increased with increasing degradation degrees, whereas those affiliated with Acidobacteria and Nitrospirae showed a decreasing pattern. Compared to bacteria, the relative abundance of most fungal phyla decreased gradually along the degradation gradient. Bacterial Shannon diversity index possessed a similar hump pattern, while fungal diversity decreased with increasing degree of grassland degradation. Bacterial and fungal communities have different responses to grassland degradation, indicating that fungi are more sensitive to grassland degradation than bacteria. Both bacterial and fungal community structures were significantly different among the three sites. Changes in soil bacterial and fungal community structures were best explained by soil salinity and pH. Plant diversity and nitrogen concentration in aboveground plant tissues were also important factors for regulating fungal communities. Co-occurrence network analysis revealed that microbial taxa increased positive interactions and average degree to strengthen the adaptability of microorganisms to grassland degradation. These findings could enhance our understanding of the formation and maintenance of microbial community diversity in degraded grasslands and the development of a new indicator for grassland ecosystem management.

With the rapid development of remote sensing technology and the growing demand for applications, the classical deep learning-based object detection model is bottlenecked in processing incremental data, especially in the increasing classes of detected objects. It requires models to sequentially learn new classes of objects based on the current model, while preserving old categories-related knowledge. Existing class-incremental detection methods achieve this goal mainly by constraining the optimization trajectory in the feature of output space. However, these works neglect the case where the previously learned background is a new category to learn, resulting in performance degradation in the new category because of the conflict between remaining the background-related knowledge or updating the background-related knowledge. This paper proposes a novel class-incremental detection method incorporated with the teacher-student architecture and the selective distillation (SDCID) strategy. Specifically, it is the asymmetry architecture, i.e., the teacher network temporarily stores historical knowledge of previously learned objects, and the student network integrates historical knowledge from the teacher network with the newly learned object-related knowledge, respectively. This asymmetry architecture reveals the significance of the distinct representation of history knowledge and new knowledge in incremental detection. Furthermore, SDCID selectively masks the shared subobject of new images to learn and previously learned background, while learning new categories of images and then transfers the classification results of the student model to the background class following the judgment model of the teacher model. This manner avoids interferences between the background category-related knowledge from a teacher model and the learning of other new classes of objects. In addition, we proposed a new incremental learning evaluation metric, C-SP, to comprehensively evaluate the incremental learning stability and plasticity performance. We verified the proposed method on two object detection datasets of remote sensing images, i.e., DIOR and DOTA. The experience results in accuracy and C-SP suggest that the proposed method surpasses existing class-incremental detection methods. We further analyzed the influence of the mask component in our method and the hyper-parameters sensitive to our method.

Abstract Background Cardiac differentiation from human pluripotent stem cells provides a unique opportunity to study human heart development in vitro and offers a potential cell source for cardiac regeneration. Compared to the large body of studies investigating cardiac maturation and cardiomyocyte subtype-specific induction, molecular events underlying cardiac lineage commitment from pluripotent stem cells at early stage remain poorly characterized. Results In order to uncover key molecular events and regulators controlling cardiac lineage commitment from a pluripotent state during differentiation, we performed single-cell RNA-Seq sequencing and obtained high-quality data for 6879 cells collected from 6 stages during cardiac differentiation from human embryonic stem cells and identified multiple cell subpopulations with distinct molecular features. Through constructing developmental trajectory of cardiac differentiation and putative ligand-receptor interactions, we revealed crosstalk between cardiac progenitor cells and endoderm cells, which could potentially provide a cellular microenvironment supporting cardiac lineage commitment at day 5. In addition, computational analyses of single-cell RNA-Seq data unveiled ETS1 (ETS Proto-Oncogene 1) activation as an important downstream event induced by crosstalk between cardiac progenitor cells and endoderm cells. Consistent with the findings from single-cell analysis, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) against ETS1 revealed genomic occupancy of ETS1 at cardiac structural genes at day 9 and day 14, whereas ETS1 depletion dramatically compromised cardiac differentiation. Conclusion Together, our study not only characterized the molecular features of different cell types and identified ETS1 as a crucial factor induced by cell-cell crosstalk contributing to cardiac lineage commitment from a pluripotent state, but may also have important implications for understanding human heart development at early embryonic stage, as well as directed manipulation of cardiac differentiation in regenerative medicine.

Enhancer RNA (eRNA) is a type of noncoding RNA transcribed from enhancer regions. Here, the authors investigate the transcriptional landscape of eRNA in cancer, incorporating pharmacogenomic data to identify potential target genes and therapeutic targets.

Abstract Background Human cancer cell lines are fundamental models for cancer research and therapeutic strategy development. However, there is no characterization of circular RNAs (circRNAs) in a large number of cancer cell lines. Methods Here, we apply four circRNA identification algorithms to heuristically characterize the expression landscape of circRNAs across ~ 1000 human cancer cell lines from CCLE polyA-enriched RNA-seq data. By using integrative analysis and experimental approaches, we explore the expression landscape, biogenesis, functional consequences, and drug response of circRNAs across different cancer lineages. Results We revealed highly lineage-specific expression patterns of circRNAs, suggesting that circRNAs may be powerful diagnostic and/or prognostic markers in cancer treatment. We also identified key genes involved in circRNA biogenesis and confirmed that TGF-β signaling may promote biogenesis of circRNAs. Strikingly, we showed that clinically actionable genes are more likely to generate circRNAs, potentially due to the enrichment of RNA-binding protein (RBP) binding sites. Among these, circMYC can promote cell proliferation. We observed strong association between the expression of circRNAs and the response to drugs, especially those targeting chromatin histone acetylation. Finally, we developed a user-friendly data portal, CircRNAs in cancer cell lines (CircRiC, https://hanlab.uth.edu/cRic), to benefit the biomedical research community. Conclusions Our study provides the characterization of circRNAs in cancer cell lines and explored the potential mechanism of circRNA biogenesis as well as its therapeutic implications. We also provide a data portal to facilitate the related biomedical researches.

When the fractional Fourier transform (FRFT) is introduced into the weak and high-dynamic global navigation satellite system (GNSS) signal acquisition, the 2-D search cell will be transferred to a 3-D one with respect to the code chip, the Doppler shift, and the Doppler rate. The proper determinations of the code bin and Doppler shift bin in the acquisition process have already been covered in the previous researches. The aim of this paper is to provide an exhaustive analysis of the approach to specify an optimal FRFT order bin, in terms of the Doppler shift rate. The lower and upper bound of FRFT order ranges is determined by the incoming signal dynamics. Then, we propose a precise model to yield an optimal FRFT order bin. Besides, a novel and fast Doppler estimator based on the non-linear least square (NLS) method is presented to improve the performance of the digital FRFT implementation. Finally, an alternate search procedure is proposed to reduce the singular estimations of the NLS method. The simulating examples demonstrate the performance of the proposed algorithms. It has been verified that the computation efficiency and the estimation accuracy have been significantly improved by proposed techniques.

Recent industry trends toward more complex and interconnected systems have increased the demand for more reliable pressure sensors. By integrating a microactuator with a pressure sensor, the sensor can self-calibrate, eliminating the complexities and costs associated with traditional sensor calibration methods to ensure reliability. The present work is focused on furthering understanding and improving the thermal performance of a thermopneumatic actuated self-calibrating pressure sensor. A transient numerical model was developed in ANSYS and was calibrated using experimental testing data. The numerical model provided insights into the sensor’s performance not previously observed in experimental testing. Furthermore, the model was utilized for two design studies. First, it was found that a substrate with low thermal conductivity and high thermal diffusivity is ideal for both the sensor’s efficiency and a faster transient response time. The second design study showed that decreasing the size of the sealed reference cavity lowers power consumption and transient response time. The study also showed that reducing the cavity base dimension has a greater effect on lowering power consumption and response time. Overall, the present work increases understanding of the self-calibrating pressure sensor and provides insight into potential design improvements, moving closer to optimized self-calibrating pressure sensors.

To better understand a role of eIF4E S209 in oncogenic translation, we generated EIF4ES209A/+ heterozygous knockin (4EKI) HCT 116 human colorectal cancer (CRC) cells. 4EKI had little impact on total eIF4E levels, cap binding or global translation, but markedly reduced HCT 116 cell growth in spheroids and mice, and CRC organoid growth. 4EKI strongly inhibited Myc and ATF4 translation, the integrated stress response (ISR)-dependent glutamine metabolic signature, AKT activation and proliferation in vivo. 4EKI inhibited polyposis in ApcMin/+ mice by suppressing Myc protein and AKT activation. Furthermore, p-eIF4E was highly elevated in CRC precursor lesions in mouse and human. p-eIF4E cooperated with mutant KRAS to promote Myc and ISR-dependent glutamine addiction in various CRC cell lines, characterized by increased cell death, transcriptomic heterogeneity and immune suppression upon deprivation. These findings demonstrate a critical role of eIF4E S209-dependent translation in Myc and stress-driven oncogenesis and as a potential therapeutic vulnerability.

Based on a 2D discrete element method (PFC2D code) and limit equilibrium method, a new method is proposed to obtain critical slip surface and to determine the factor of safety of the slope model. After the calibration process, the excavation process on the rectangular assembly of particles can make the slope model of the DEM. As the trial acceleration of gravity increases, the critical slip surface tends to change from shallow to deep, which indicates more serious failures in the slope. With higher trial accelerations of gravity, the factor of safety calculated by this method continually increases as well. Therefore, the critical trial acceleration of gravity that places the slope in a critical state of failure is essential to ensure the results are correct. The gravity increase method is applied to render the critical state of failure for slope model. As soon as the continuous failure surface forms, the influence parameters of particles on the critical slip surface also can be determined. Connecting the centre of these particles forms the critical slip surface, which is more consistent with the actual situation in nature due to the absence of assumptions of the critical slip surface in this method. Then, the factor of safety is calculated by the Spencer method that has the ability of dealing with noncircular, complex slip surface. This method proves to be a promising tool to analyse slope stability with the DEM. The character of progressive failure process of slope can be well modelled by DEM by investigating the development of failure in the slope body. By studying temporal and spatial risk of slope instability by the total failure process, it is possible for managers and engineers to detect temporal and spatial risks so robust suggestions and mitigation measures can be made.

Actinomycin (Act) V, an analogue of Act D, presented stronger antitumor activity and less hepatorenal toxicity than Act D in our previous studies, which is worthy of further investigation. We hereby report that Act V induces apoptosis via mitochondrial and PI3K/AKT pathways in colorectal cancer (CRC) cells. Act V-induced apoptosis was characterized by mitochondrial dysfunction, with loss of mitochondria membrane potential (MMP) and cytochrome c release, which then activated cleaved caspase-9, cleaved caspase-3, and cleaved PARP, revealing that it was related to the mitochondrial pathway, and the apoptotic trendency can be reversed by caspase inhibitor Z-VAD-FMK. Furthermore, we proved that Act V significantly inhibited PI3K/AKT signalling in HCT-116 cells using cell experiments in vitro, and it also presented a potential targeted PI3Kα inhibition using computer docking models. Further elucidation revealed that it exhibited a 28-fold greater potency than the PI3K inhibitor LY294002 on PI3K inhibition efficacy. Taken together, Act V, as a superior potential replacement of Act D, is a potential candidate for inhibiting the PI3K/AKT pathway and is worthy of more pre-clinical studies in the therapy of CRC.

A free space vortex light generator is designed based on the diffraction of an optical Bessel beam. The Bessel beam propagating in a single mode fiber is modulated by a helical phase disk designed at the fiber end and gradually evolves to vortex light in free space. The properties of the generator such as operation wavelength range, polarization sensitivity, and fabrication tolerance are analyzed using finite-difference time-domain software. With the inherent advantage of the “nondiffraction” feature of the Bessel beam naturally obtained from an optical fiber, the proposed fiber vortex light generator would have potential applications in high-resolution imaging, particle trapping, manipulation, and so on.

Objective This study was performed to identify a reasonable cutoff age for defining older patients with colorectal cancer (CRC) and to examine whether old age was related with increased colorectal cancer-specific death (CSD) and poor colorectal cancer-specific survival (CSS). Methods A total of 76,858 eligible patients from the surveillance, epidemiology, and end results (SEER) database were included in this study. The Cox proportional hazard regression model and the Chow test were used to determine a suitable cutoff age for defining the older group. Furthermore, a propensity score matching analysis was performed to adjust for heterogeneity between groups. A competing risk regression model was used to explore the impact of age on CSD and non-colorectal cancer-specific death (non-CSD). Kaplan–Meier survival curves were plotted to compare CSS between groups. Also, a Cox regression model was used to validate the results. External validation was performed on data from 1998 to 2003 retrieved from the SEER database. Results Based on a cutoff age of 70 years, the examined cohort of patients was classified into a younger group (n = 51,915,

PurposeAnal adenocarcinoma (AA) represents a rare condition, and little is known about the predictive factors of the outcomes or the optimal TNM staging system for curable AA. Using population-based data, we preliminarily sought to determine the prognostic factors and evaluate the existing T and N staging criteria of AA.MethodsWe analyzed the Surveillance, Epidemiology, and End Results 18 database to identify patients 20-80 years old who were diagnosed with AA or rectal adenocarcinoma (RA) and underwent abdominal perineal resection between 2004 and 2012. The difference between Kaplan-Meier survival curves was estimated by a log-rank test. A Cox proportional hazard regression model was used to adjust the effects of other covariates on survival in the propensity score-matched cohort, including age, gender, race, marital status, histology, grade of differentiation, tumor size, number of positive lymph nodes, radiotherapy, and chemotherapy.ResultsCompared to patients with RA, patients with AA had a worse CSS after controlling for other covariates (hazard ratio [HR], 1.96; 95% confidence interval [CI], 1.25-3.07; P0.05; >5 cm: HR, 1.01; 95% CI, 0.49-2.07; P>0.05) had no significant influence on survival. The number of positive lymph nodes (1-3: HR, 2.93; 95% CI, 1.55-5.53; PConclusionsAA confers a worse prognosis than RA does. The T staging criteria of anal carcinoma, dominated by tumor size, seem to be invalid for AA, while the number of positive lymph nodes is a prognostic factor.

A data offloading strategy for optimizing itinerary planning of MA（mobile agent）was proposed. When MA visits source nodes based on data offloading strategy，it decides whether to transfer data package via optimized offloading itinerary and MA（mobile agent）visits data source nodes via conventional itinerary. Based on considerable simulations with the representative MA itinerary planning，i.e. itinerary energy minimum for first-source-selection （IEMF），it verifies that the problem of excessive energy consumption from source nodes can be efficiently solved and the lifetime of source nodes is prolonged.

The high toxicity of actinomycin D (Act D) severely limits its use as a first-line chemotherapeutic agent in the clinic. Actinomycin V (Act V), an analog of Act D, exhibited strong anticancer activity in our previous studies. Here, we provide evidence that Act V has less hepatorenal toxicity than Act D in vitro and in vivo, associated with the reactive oxygen species (ROS) pathway. Compared to Act D, Act V exhibited considerably stronger sensitivity for cancer cells and less toxicity to human normal liver LO-2 and human embryonic kidney 293T cells using the MTT (3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide) assay. Notably, Act V caused less damage to both the liver and kidney than Act D in vivo, indicated by organ to body weight ratios, as well as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and serum creatinine (Scr) levels. Further experiments showed that the ROS pathway is involved in Act V-induced hepatorenal toxicity. Act V generates ROS and accumulates malondialdehyde (MDA), reducing levels of superoxide dismutase (SOD) and glutathione (GSH) in LO-2 and 293T cells. These findings indicate that Act V induces less hepatorenal toxicity than Act D in vitro and in vivo and merits further development as a potential therapeutic agent for the treatment of cancer.

For vehicle positioning with Global Navigation Satellite System (GNSS) in urban areas, open-loop tracking shows better performance because of its high sensitivity and superior robustness against multipath. However, no previous study has focused on the effects of the code search grid size on the code phase measurement accuracy of open-loop tracking. Traditional open-loop tracking methods are performed by the batch correlators with fixed correlation space. The code search grid size, which is the correlation space, is a constant empirical value and the code phase measuring accuracy will be largely degraded due to the improper grid size, especially when the signal carrier-to-noise density ratio (C/N0) varies. In this study, the Adaptive Correlation Space Adjusted Open-Loop Tracking Approach (ACSA-OLTA) is proposed to improve the code phase measurement dependent pseudo range accuracy. In ACSA-OLTA, the correlation space is adjusted according to the signal C/N0. The novel Equivalent Weighted Pseudo Range Error (EWPRE) is raised to obtain the optimal code search grid sizes for different C/N0. The code phase measuring errors of different measurement calculation methods are analyzed for the first time. The measurement calculation strategy of ACSA-OLTA is derived from the analysis to further improve the accuracy but reduce the correlator consumption. Performance simulation and real tests confirm that the pseudo range and positioning accuracy of ASCA-OLTA are better than the traditional open-loop tracking methods in the usual scenarios of urban area.